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Microsoft.MachineLearningServices workspaces/schedules 2023-04-01-preview

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Bicep resource definition

The workspaces/schedules resource type can be deployed with operations that target:

For a list of changed properties in each API version, see change log.

Resource format

To create a Microsoft.MachineLearningServices/workspaces/schedules resource, add the following Bicep to your template.

resource symbolicname 'Microsoft.MachineLearningServices/workspaces/schedules@2023-04-01-preview' = {
  name: 'string'
  parent: resourceSymbolicName
  properties: {
    action: {
      actionType: 'string'
      // For remaining properties, see ScheduleActionBase objects
    }
    description: 'string'
    displayName: 'string'
    isEnabled: bool
    properties: {
      {customized property}: 'string'
    }
    tags: {}
    trigger: {
      endTime: 'string'
      startTime: 'string'
      timeZone: 'string'
      triggerType: 'string'
      // For remaining properties, see TriggerBase objects
    }
  }
}

ScheduleActionBase objects

Set the actionType property to specify the type of object.

For CreateJob, use:

  actionType: 'CreateJob'
  jobDefinition: {
    componentId: 'string'
    computeId: 'string'
    description: 'string'
    displayName: 'string'
    experimentName: 'string'
    identity: {
      identityType: 'string'
      // For remaining properties, see IdentityConfiguration objects
    }
    isArchived: bool
    notificationSetting: {
      emailOn: [
        'string'
      ]
      emails: [
        'string'
      ]
      webhooks: {
        {customized property}: {
          eventType: 'string'
          webhookType: 'string'
          // For remaining properties, see Webhook objects
        }
      }
    }
    properties: {
      {customized property}: 'string'
    }
    secretsConfiguration: {
      {customized property}: {
        uri: 'string'
        workspaceSecretName: 'string'
      }
    }
    services: {
      {customized property}: {
        endpoint: 'string'
        jobServiceType: 'string'
        nodes: {
          nodesValueType: 'string'
          // For remaining properties, see Nodes objects
        }
        port: int
        properties: {
          {customized property}: 'string'
        }
      }
    }
    tags: {}
    jobType: 'string'
    // For remaining properties, see JobBaseProperties objects
  }

For CreateMonitor, use:

  actionType: 'CreateMonitor'
  monitorDefinition: {
    alertNotificationSetting: {
      alertNotificationType: 'string'
      // For remaining properties, see MonitoringAlertNotificationSettingsBase objects
    }
    computeId: 'string'
    monitoringTarget: 'string'
    signals: {
      {customized property}: {
        lookbackPeriod: 'string'
        mode: 'string'
        signalType: 'string'
        // For remaining properties, see MonitoringSignalBase objects
      }
    }
  }

For ImportData, use:

  actionType: 'ImportData'
  dataImportDefinition: {
    assetName: 'string'
    autoDeleteSetting: {
      condition: 'string'
      value: 'string'
    }
    dataType: 'string'
    dataUri: 'string'
    description: 'string'
    intellectualProperty: {
      protectionLevel: 'string'
      publisher: 'string'
    }
    isAnonymous: bool
    isArchived: bool
    properties: {
      {customized property}: 'string'
    }
    source: {
      connection: 'string'
      sourceType: 'string'
      // For remaining properties, see DataImportSource objects
    }
    stage: 'string'
    tags: {}
  }

For InvokeBatchEndpoint, use:

  actionType: 'InvokeBatchEndpoint'
  endpointInvocationDefinition: any()

JobBaseProperties objects

Set the jobType property to specify the type of object.

For AutoML, use:

  jobType: 'AutoML'
  environmentId: 'string'
  environmentVariables: {
    {customized property}: 'string'
  }
  outputs: {
    {customized property}: {
      description: 'string'
      jobOutputType: 'string'
      // For remaining properties, see JobOutput objects
    }
  }
  queueSettings: {
    jobTier: 'string'
    priority: int
  }
  resources: {
    dockerArgs: 'string'
    instanceCount: int
    instanceType: 'string'
    locations: [
      'string'
    ]
    maxInstanceCount: int
    properties: {
      {customized property}: any()
    }
    shmSize: 'string'
  }
  taskDetails: {
    logVerbosity: 'string'
    targetColumnName: 'string'
    trainingData: {
      description: 'string'
      jobInputType: 'string'
      mode: 'string'
      uri: 'string'
    }
    taskType: 'string'
    // For remaining properties, see AutoMLVertical objects
  }

For Command, use:

  jobType: 'Command'
  autologgerSettings: {
    mlflowAutologger: 'string'
  }
  codeId: 'string'
  command: 'string'
  distribution: {
    distributionType: 'string'
    // For remaining properties, see DistributionConfiguration objects
  }
  environmentId: 'string'
  environmentVariables: {
    {customized property}: 'string'
  }
  inputs: {
    {customized property}: {
      description: 'string'
      jobInputType: 'string'
      // For remaining properties, see JobInput objects
    }
  }
  limits: {
    jobLimitsType: 'string'
    timeout: 'string'
  }
  outputs: {
    {customized property}: {
      description: 'string'
      jobOutputType: 'string'
      // For remaining properties, see JobOutput objects
    }
  }
  queueSettings: {
    jobTier: 'string'
    priority: int
  }
  resources: {
    dockerArgs: 'string'
    instanceCount: int
    instanceType: 'string'
    locations: [
      'string'
    ]
    maxInstanceCount: int
    properties: {
      {customized property}: any()
    }
    shmSize: 'string'
  }

For Labeling, use:

  jobType: 'Labeling'
  dataConfiguration: {
    dataId: 'string'
    incrementalDataRefresh: 'string'
  }
  jobInstructions: {
    uri: 'string'
  }
  labelCategories: {
    {customized property}: {
      classes: {
        {customized property}: {
          displayName: 'string'
          subclasses: {
            {customized property}: {}
        }
      }
      displayName: 'string'
      multiSelect: 'string'
    }
  }
  labelingJobMediaProperties: {
    mediaType: 'string'
    // For remaining properties, see LabelingJobMediaProperties objects
  }
  mlAssistConfiguration: {
    mlAssist: 'string'
    // For remaining properties, see MLAssistConfiguration objects
  }

For Pipeline, use:

  jobType: 'Pipeline'
  inputs: {
    {customized property}: {
      description: 'string'
      jobInputType: 'string'
      // For remaining properties, see JobInput objects
    }
  }
  jobs: {
    {customized property}: any()
  }
  outputs: {
    {customized property}: {
      description: 'string'
      jobOutputType: 'string'
      // For remaining properties, see JobOutput objects
    }
  }
  settings: any()
  sourceJobId: 'string'

For Spark, use:

  jobType: 'Spark'
  archives: [
    'string'
  ]
  args: 'string'
  codeId: 'string'
  conf: {
    {customized property}: 'string'
  }
  entry: {
    sparkJobEntryType: 'string'
    // For remaining properties, see SparkJobEntry objects
  }
  environmentId: 'string'
  files: [
    'string'
  ]
  inputs: {
    {customized property}: {
      description: 'string'
      jobInputType: 'string'
      // For remaining properties, see JobInput objects
    }
  }
  jars: [
    'string'
  ]
  outputs: {
    {customized property}: {
      description: 'string'
      jobOutputType: 'string'
      // For remaining properties, see JobOutput objects
    }
  }
  pyFiles: [
    'string'
  ]
  queueSettings: {
    jobTier: 'string'
    priority: int
  }
  resources: {
    instanceType: 'string'
    runtimeVersion: 'string'
  }

For Sweep, use:

  jobType: 'Sweep'
  earlyTermination: {
    delayEvaluation: int
    evaluationInterval: int
    policyType: 'string'
    // For remaining properties, see EarlyTerminationPolicy objects
  }
  inputs: {
    {customized property}: {
      description: 'string'
      jobInputType: 'string'
      // For remaining properties, see JobInput objects
    }
  }
  limits: {
    jobLimitsType: 'string'
    maxConcurrentTrials: int
    maxTotalTrials: int
    timeout: 'string'
    trialTimeout: 'string'
  }
  objective: {
    goal: 'string'
    primaryMetric: 'string'
  }
  outputs: {
    {customized property}: {
      description: 'string'
      jobOutputType: 'string'
      // For remaining properties, see JobOutput objects
    }
  }
  queueSettings: {
    jobTier: 'string'
    priority: int
  }
  samplingAlgorithm: {
    samplingAlgorithmType: 'string'
    // For remaining properties, see SamplingAlgorithm objects
  }
  searchSpace: any()
  trial: {
    codeId: 'string'
    command: 'string'
    distribution: {
      distributionType: 'string'
      // For remaining properties, see DistributionConfiguration objects
    }
    environmentId: 'string'
    environmentVariables: {
      {customized property}: 'string'
    }
    resources: {
      dockerArgs: 'string'
      instanceCount: int
      instanceType: 'string'
      locations: [
        'string'
      ]
      maxInstanceCount: int
      properties: {
        {customized property}: any()
      }
      shmSize: 'string'
    }
  }

IdentityConfiguration objects

Set the identityType property to specify the type of object.

For AMLToken, use:

  identityType: 'AMLToken'

For Managed, use:

  identityType: 'Managed'
  clientId: 'string'
  objectId: 'string'
  resourceId: 'string'

For UserIdentity, use:

  identityType: 'UserIdentity'

Webhook objects

Set the webhookType property to specify the type of object.

For AzureDevOps, use:

  webhookType: 'AzureDevOps'

Nodes objects

Set the nodesValueType property to specify the type of object.

For All, use:

  nodesValueType: 'All'

JobOutput objects

Set the jobOutputType property to specify the type of object.

For custom_model, use:

  jobOutputType: 'custom_model'
  assetName: 'string'
  assetVersion: 'string'
  autoDeleteSetting: {
    condition: 'string'
    value: 'string'
  }
  mode: 'string'
  uri: 'string'

For mlflow_model, use:

  jobOutputType: 'mlflow_model'
  assetName: 'string'
  assetVersion: 'string'
  autoDeleteSetting: {
    condition: 'string'
    value: 'string'
  }
  mode: 'string'
  uri: 'string'

For mltable, use:

  jobOutputType: 'mltable'
  assetName: 'string'
  assetVersion: 'string'
  autoDeleteSetting: {
    condition: 'string'
    value: 'string'
  }
  mode: 'string'
  uri: 'string'

For triton_model, use:

  jobOutputType: 'triton_model'
  assetName: 'string'
  assetVersion: 'string'
  autoDeleteSetting: {
    condition: 'string'
    value: 'string'
  }
  mode: 'string'
  uri: 'string'

For uri_file, use:

  jobOutputType: 'uri_file'
  assetName: 'string'
  assetVersion: 'string'
  autoDeleteSetting: {
    condition: 'string'
    value: 'string'
  }
  mode: 'string'
  uri: 'string'

For uri_folder, use:

  jobOutputType: 'uri_folder'
  assetName: 'string'
  assetVersion: 'string'
  autoDeleteSetting: {
    condition: 'string'
    value: 'string'
  }
  mode: 'string'
  uri: 'string'

AutoMLVertical objects

Set the taskType property to specify the type of object.

For Classification, use:

  taskType: 'Classification'
  cvSplitColumnNames: [
    'string'
  ]
  featurizationSettings: {
    blockedTransformers: [
      'string'
    ]
    columnNameAndTypes: {
      {customized property}: 'string'
    }
    datasetLanguage: 'string'
    enableDnnFeaturization: bool
    mode: 'string'
    transformerParams: {
      {customized property}: [
        {
          fields: [
            'string'
          ]
          parameters: any()
        }
      ]
    }
  }
  fixedParameters: {
    booster: 'string'
    boostingType: 'string'
    growPolicy: 'string'
    learningRate: int
    maxBin: int
    maxDepth: int
    maxLeaves: int
    minDataInLeaf: int
    minSplitGain: int
    modelName: 'string'
    nEstimators: int
    numLeaves: int
    preprocessorName: 'string'
    regAlpha: int
    regLambda: int
    subsample: int
    subsampleFreq: int
    treeMethod: 'string'
    withMean: bool
    withStd: bool
  }
  limitSettings: {
    enableEarlyTermination: bool
    exitScore: int
    maxConcurrentTrials: int
    maxCoresPerTrial: int
    maxNodes: int
    maxTrials: int
    sweepConcurrentTrials: int
    sweepTrials: int
    timeout: 'string'
    trialTimeout: 'string'
  }
  nCrossValidations: {
    mode: 'string'
    // For remaining properties, see NCrossValidations objects
  }
  positiveLabel: 'string'
  primaryMetric: 'string'
  searchSpace: [
    {
      booster: 'string'
      boostingType: 'string'
      growPolicy: 'string'
      learningRate: 'string'
      maxBin: 'string'
      maxDepth: 'string'
      maxLeaves: 'string'
      minDataInLeaf: 'string'
      minSplitGain: 'string'
      modelName: 'string'
      nEstimators: 'string'
      numLeaves: 'string'
      preprocessorName: 'string'
      regAlpha: 'string'
      regLambda: 'string'
      subsample: 'string'
      subsampleFreq: 'string'
      treeMethod: 'string'
      withMean: 'string'
      withStd: 'string'
    }
  ]
  sweepSettings: {
    earlyTermination: {
      delayEvaluation: int
      evaluationInterval: int
      policyType: 'string'
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm: 'string'
  }
  testData: {
    description: 'string'
    jobInputType: 'string'
    mode: 'string'
    uri: 'string'
  }
  testDataSize: int
  trainingSettings: {
    allowedTrainingAlgorithms: [
      'string'
    ]
    blockedTrainingAlgorithms: [
      'string'
    ]
    enableDnnTraining: bool
    enableModelExplainability: bool
    enableOnnxCompatibleModels: bool
    enableStackEnsemble: bool
    enableVoteEnsemble: bool
    ensembleModelDownloadTimeout: 'string'
    stackEnsembleSettings: {
      stackMetaLearnerKWargs: any()
      stackMetaLearnerTrainPercentage: int
      stackMetaLearnerType: 'string'
    }
    trainingMode: 'string'
  }
  validationData: {
    description: 'string'
    jobInputType: 'string'
    mode: 'string'
    uri: 'string'
  }
  validationDataSize: int
  weightColumnName: 'string'

For Forecasting, use:

  taskType: 'Forecasting'
  cvSplitColumnNames: [
    'string'
  ]
  featurizationSettings: {
    blockedTransformers: [
      'string'
    ]
    columnNameAndTypes: {
      {customized property}: 'string'
    }
    datasetLanguage: 'string'
    enableDnnFeaturization: bool
    mode: 'string'
    transformerParams: {
      {customized property}: [
        {
          fields: [
            'string'
          ]
          parameters: any()
        }
      ]
    }
  }
  fixedParameters: {
    booster: 'string'
    boostingType: 'string'
    growPolicy: 'string'
    learningRate: int
    maxBin: int
    maxDepth: int
    maxLeaves: int
    minDataInLeaf: int
    minSplitGain: int
    modelName: 'string'
    nEstimators: int
    numLeaves: int
    preprocessorName: 'string'
    regAlpha: int
    regLambda: int
    subsample: int
    subsampleFreq: int
    treeMethod: 'string'
    withMean: bool
    withStd: bool
  }
  forecastingSettings: {
    countryOrRegionForHolidays: 'string'
    cvStepSize: int
    featureLags: 'string'
    featuresUnknownAtForecastTime: [
      'string'
    ]
    forecastHorizon: {
      mode: 'string'
      // For remaining properties, see ForecastHorizon objects
    }
    frequency: 'string'
    seasonality: {
      mode: 'string'
      // For remaining properties, see Seasonality objects
    }
    shortSeriesHandlingConfig: 'string'
    targetAggregateFunction: 'string'
    targetLags: {
      mode: 'string'
      // For remaining properties, see TargetLags objects
    }
    targetRollingWindowSize: {
      mode: 'string'
      // For remaining properties, see TargetRollingWindowSize objects
    }
    timeColumnName: 'string'
    timeSeriesIdColumnNames: [
      'string'
    ]
    useStl: 'string'
  }
  limitSettings: {
    enableEarlyTermination: bool
    exitScore: int
    maxConcurrentTrials: int
    maxCoresPerTrial: int
    maxNodes: int
    maxTrials: int
    sweepConcurrentTrials: int
    sweepTrials: int
    timeout: 'string'
    trialTimeout: 'string'
  }
  nCrossValidations: {
    mode: 'string'
    // For remaining properties, see NCrossValidations objects
  }
  primaryMetric: 'string'
  searchSpace: [
    {
      booster: 'string'
      boostingType: 'string'
      growPolicy: 'string'
      learningRate: 'string'
      maxBin: 'string'
      maxDepth: 'string'
      maxLeaves: 'string'
      minDataInLeaf: 'string'
      minSplitGain: 'string'
      modelName: 'string'
      nEstimators: 'string'
      numLeaves: 'string'
      preprocessorName: 'string'
      regAlpha: 'string'
      regLambda: 'string'
      subsample: 'string'
      subsampleFreq: 'string'
      treeMethod: 'string'
      withMean: 'string'
      withStd: 'string'
    }
  ]
  sweepSettings: {
    earlyTermination: {
      delayEvaluation: int
      evaluationInterval: int
      policyType: 'string'
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm: 'string'
  }
  testData: {
    description: 'string'
    jobInputType: 'string'
    mode: 'string'
    uri: 'string'
  }
  testDataSize: int
  trainingSettings: {
    allowedTrainingAlgorithms: [
      'string'
    ]
    blockedTrainingAlgorithms: [
      'string'
    ]
    enableDnnTraining: bool
    enableModelExplainability: bool
    enableOnnxCompatibleModels: bool
    enableStackEnsemble: bool
    enableVoteEnsemble: bool
    ensembleModelDownloadTimeout: 'string'
    stackEnsembleSettings: {
      stackMetaLearnerKWargs: any()
      stackMetaLearnerTrainPercentage: int
      stackMetaLearnerType: 'string'
    }
    trainingMode: 'string'
  }
  validationData: {
    description: 'string'
    jobInputType: 'string'
    mode: 'string'
    uri: 'string'
  }
  validationDataSize: int
  weightColumnName: 'string'

For ImageClassification, use:

  taskType: 'ImageClassification'
  limitSettings: {
    maxConcurrentTrials: int
    maxTrials: int
    timeout: 'string'
  }
  modelSettings: {
    advancedSettings: 'string'
    amsGradient: bool
    augmentations: 'string'
    beta1: int
    beta2: int
    checkpointFrequency: int
    checkpointModel: {
      description: 'string'
      jobInputType: 'string'
      mode: 'string'
      uri: 'string'
    }
    checkpointRunId: 'string'
    distributed: bool
    earlyStopping: bool
    earlyStoppingDelay: int
    earlyStoppingPatience: int
    enableOnnxNormalization: bool
    evaluationFrequency: int
    gradientAccumulationStep: int
    layersToFreeze: int
    learningRate: int
    learningRateScheduler: 'string'
    modelName: 'string'
    momentum: int
    nesterov: bool
    numberOfEpochs: int
    numberOfWorkers: int
    optimizer: 'string'
    randomSeed: int
    stepLRGamma: int
    stepLRStepSize: int
    trainingBatchSize: int
    trainingCropSize: int
    validationBatchSize: int
    validationCropSize: int
    validationResizeSize: int
    warmupCosineLRCycles: int
    warmupCosineLRWarmupEpochs: int
    weightDecay: int
    weightedLoss: int
  }
  primaryMetric: 'string'
  searchSpace: [
    {
      amsGradient: 'string'
      augmentations: 'string'
      beta1: 'string'
      beta2: 'string'
      distributed: 'string'
      earlyStopping: 'string'
      earlyStoppingDelay: 'string'
      earlyStoppingPatience: 'string'
      enableOnnxNormalization: 'string'
      evaluationFrequency: 'string'
      gradientAccumulationStep: 'string'
      layersToFreeze: 'string'
      learningRate: 'string'
      learningRateScheduler: 'string'
      modelName: 'string'
      momentum: 'string'
      nesterov: 'string'
      numberOfEpochs: 'string'
      numberOfWorkers: 'string'
      optimizer: 'string'
      randomSeed: 'string'
      stepLRGamma: 'string'
      stepLRStepSize: 'string'
      trainingBatchSize: 'string'
      trainingCropSize: 'string'
      validationBatchSize: 'string'
      validationCropSize: 'string'
      validationResizeSize: 'string'
      warmupCosineLRCycles: 'string'
      warmupCosineLRWarmupEpochs: 'string'
      weightDecay: 'string'
      weightedLoss: 'string'
    }
  ]
  sweepSettings: {
    earlyTermination: {
      delayEvaluation: int
      evaluationInterval: int
      policyType: 'string'
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm: 'string'
  }
  validationData: {
    description: 'string'
    jobInputType: 'string'
    mode: 'string'
    uri: 'string'
  }
  validationDataSize: int

For ImageClassificationMultilabel, use:

  taskType: 'ImageClassificationMultilabel'
  limitSettings: {
    maxConcurrentTrials: int
    maxTrials: int
    timeout: 'string'
  }
  modelSettings: {
    advancedSettings: 'string'
    amsGradient: bool
    augmentations: 'string'
    beta1: int
    beta2: int
    checkpointFrequency: int
    checkpointModel: {
      description: 'string'
      jobInputType: 'string'
      mode: 'string'
      uri: 'string'
    }
    checkpointRunId: 'string'
    distributed: bool
    earlyStopping: bool
    earlyStoppingDelay: int
    earlyStoppingPatience: int
    enableOnnxNormalization: bool
    evaluationFrequency: int
    gradientAccumulationStep: int
    layersToFreeze: int
    learningRate: int
    learningRateScheduler: 'string'
    modelName: 'string'
    momentum: int
    nesterov: bool
    numberOfEpochs: int
    numberOfWorkers: int
    optimizer: 'string'
    randomSeed: int
    stepLRGamma: int
    stepLRStepSize: int
    trainingBatchSize: int
    trainingCropSize: int
    validationBatchSize: int
    validationCropSize: int
    validationResizeSize: int
    warmupCosineLRCycles: int
    warmupCosineLRWarmupEpochs: int
    weightDecay: int
    weightedLoss: int
  }
  primaryMetric: 'string'
  searchSpace: [
    {
      amsGradient: 'string'
      augmentations: 'string'
      beta1: 'string'
      beta2: 'string'
      distributed: 'string'
      earlyStopping: 'string'
      earlyStoppingDelay: 'string'
      earlyStoppingPatience: 'string'
      enableOnnxNormalization: 'string'
      evaluationFrequency: 'string'
      gradientAccumulationStep: 'string'
      layersToFreeze: 'string'
      learningRate: 'string'
      learningRateScheduler: 'string'
      modelName: 'string'
      momentum: 'string'
      nesterov: 'string'
      numberOfEpochs: 'string'
      numberOfWorkers: 'string'
      optimizer: 'string'
      randomSeed: 'string'
      stepLRGamma: 'string'
      stepLRStepSize: 'string'
      trainingBatchSize: 'string'
      trainingCropSize: 'string'
      validationBatchSize: 'string'
      validationCropSize: 'string'
      validationResizeSize: 'string'
      warmupCosineLRCycles: 'string'
      warmupCosineLRWarmupEpochs: 'string'
      weightDecay: 'string'
      weightedLoss: 'string'
    }
  ]
  sweepSettings: {
    earlyTermination: {
      delayEvaluation: int
      evaluationInterval: int
      policyType: 'string'
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm: 'string'
  }
  validationData: {
    description: 'string'
    jobInputType: 'string'
    mode: 'string'
    uri: 'string'
  }
  validationDataSize: int

For ImageInstanceSegmentation, use:

  taskType: 'ImageInstanceSegmentation'
  limitSettings: {
    maxConcurrentTrials: int
    maxTrials: int
    timeout: 'string'
  }
  modelSettings: {
    advancedSettings: 'string'
    amsGradient: bool
    augmentations: 'string'
    beta1: int
    beta2: int
    boxDetectionsPerImage: int
    boxScoreThreshold: int
    checkpointFrequency: int
    checkpointModel: {
      description: 'string'
      jobInputType: 'string'
      mode: 'string'
      uri: 'string'
    }
    checkpointRunId: 'string'
    distributed: bool
    earlyStopping: bool
    earlyStoppingDelay: int
    earlyStoppingPatience: int
    enableOnnxNormalization: bool
    evaluationFrequency: int
    gradientAccumulationStep: int
    imageSize: int
    layersToFreeze: int
    learningRate: int
    learningRateScheduler: 'string'
    logTrainingMetrics: 'string'
    logValidationLoss: 'string'
    maxSize: int
    minSize: int
    modelName: 'string'
    modelSize: 'string'
    momentum: int
    multiScale: bool
    nesterov: bool
    nmsIouThreshold: int
    numberOfEpochs: int
    numberOfWorkers: int
    optimizer: 'string'
    randomSeed: int
    stepLRGamma: int
    stepLRStepSize: int
    tileGridSize: 'string'
    tileOverlapRatio: int
    tilePredictionsNmsThreshold: int
    trainingBatchSize: int
    validationBatchSize: int
    validationIouThreshold: int
    validationMetricType: 'string'
    warmupCosineLRCycles: int
    warmupCosineLRWarmupEpochs: int
    weightDecay: int
  }
  primaryMetric: 'MeanAveragePrecision'
  searchSpace: [
    {
      amsGradient: 'string'
      augmentations: 'string'
      beta1: 'string'
      beta2: 'string'
      boxDetectionsPerImage: 'string'
      boxScoreThreshold: 'string'
      distributed: 'string'
      earlyStopping: 'string'
      earlyStoppingDelay: 'string'
      earlyStoppingPatience: 'string'
      enableOnnxNormalization: 'string'
      evaluationFrequency: 'string'
      gradientAccumulationStep: 'string'
      imageSize: 'string'
      layersToFreeze: 'string'
      learningRate: 'string'
      learningRateScheduler: 'string'
      maxSize: 'string'
      minSize: 'string'
      modelName: 'string'
      modelSize: 'string'
      momentum: 'string'
      multiScale: 'string'
      nesterov: 'string'
      nmsIouThreshold: 'string'
      numberOfEpochs: 'string'
      numberOfWorkers: 'string'
      optimizer: 'string'
      randomSeed: 'string'
      stepLRGamma: 'string'
      stepLRStepSize: 'string'
      tileGridSize: 'string'
      tileOverlapRatio: 'string'
      tilePredictionsNmsThreshold: 'string'
      trainingBatchSize: 'string'
      validationBatchSize: 'string'
      validationIouThreshold: 'string'
      validationMetricType: 'string'
      warmupCosineLRCycles: 'string'
      warmupCosineLRWarmupEpochs: 'string'
      weightDecay: 'string'
    }
  ]
  sweepSettings: {
    earlyTermination: {
      delayEvaluation: int
      evaluationInterval: int
      policyType: 'string'
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm: 'string'
  }
  validationData: {
    description: 'string'
    jobInputType: 'string'
    mode: 'string'
    uri: 'string'
  }
  validationDataSize: int

For ImageObjectDetection, use:

  taskType: 'ImageObjectDetection'
  limitSettings: {
    maxConcurrentTrials: int
    maxTrials: int
    timeout: 'string'
  }
  modelSettings: {
    advancedSettings: 'string'
    amsGradient: bool
    augmentations: 'string'
    beta1: int
    beta2: int
    boxDetectionsPerImage: int
    boxScoreThreshold: int
    checkpointFrequency: int
    checkpointModel: {
      description: 'string'
      jobInputType: 'string'
      mode: 'string'
      uri: 'string'
    }
    checkpointRunId: 'string'
    distributed: bool
    earlyStopping: bool
    earlyStoppingDelay: int
    earlyStoppingPatience: int
    enableOnnxNormalization: bool
    evaluationFrequency: int
    gradientAccumulationStep: int
    imageSize: int
    layersToFreeze: int
    learningRate: int
    learningRateScheduler: 'string'
    logTrainingMetrics: 'string'
    logValidationLoss: 'string'
    maxSize: int
    minSize: int
    modelName: 'string'
    modelSize: 'string'
    momentum: int
    multiScale: bool
    nesterov: bool
    nmsIouThreshold: int
    numberOfEpochs: int
    numberOfWorkers: int
    optimizer: 'string'
    randomSeed: int
    stepLRGamma: int
    stepLRStepSize: int
    tileGridSize: 'string'
    tileOverlapRatio: int
    tilePredictionsNmsThreshold: int
    trainingBatchSize: int
    validationBatchSize: int
    validationIouThreshold: int
    validationMetricType: 'string'
    warmupCosineLRCycles: int
    warmupCosineLRWarmupEpochs: int
    weightDecay: int
  }
  primaryMetric: 'MeanAveragePrecision'
  searchSpace: [
    {
      amsGradient: 'string'
      augmentations: 'string'
      beta1: 'string'
      beta2: 'string'
      boxDetectionsPerImage: 'string'
      boxScoreThreshold: 'string'
      distributed: 'string'
      earlyStopping: 'string'
      earlyStoppingDelay: 'string'
      earlyStoppingPatience: 'string'
      enableOnnxNormalization: 'string'
      evaluationFrequency: 'string'
      gradientAccumulationStep: 'string'
      imageSize: 'string'
      layersToFreeze: 'string'
      learningRate: 'string'
      learningRateScheduler: 'string'
      maxSize: 'string'
      minSize: 'string'
      modelName: 'string'
      modelSize: 'string'
      momentum: 'string'
      multiScale: 'string'
      nesterov: 'string'
      nmsIouThreshold: 'string'
      numberOfEpochs: 'string'
      numberOfWorkers: 'string'
      optimizer: 'string'
      randomSeed: 'string'
      stepLRGamma: 'string'
      stepLRStepSize: 'string'
      tileGridSize: 'string'
      tileOverlapRatio: 'string'
      tilePredictionsNmsThreshold: 'string'
      trainingBatchSize: 'string'
      validationBatchSize: 'string'
      validationIouThreshold: 'string'
      validationMetricType: 'string'
      warmupCosineLRCycles: 'string'
      warmupCosineLRWarmupEpochs: 'string'
      weightDecay: 'string'
    }
  ]
  sweepSettings: {
    earlyTermination: {
      delayEvaluation: int
      evaluationInterval: int
      policyType: 'string'
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm: 'string'
  }
  validationData: {
    description: 'string'
    jobInputType: 'string'
    mode: 'string'
    uri: 'string'
  }
  validationDataSize: int

For Regression, use:

  taskType: 'Regression'
  cvSplitColumnNames: [
    'string'
  ]
  featurizationSettings: {
    blockedTransformers: [
      'string'
    ]
    columnNameAndTypes: {
      {customized property}: 'string'
    }
    datasetLanguage: 'string'
    enableDnnFeaturization: bool
    mode: 'string'
    transformerParams: {
      {customized property}: [
        {
          fields: [
            'string'
          ]
          parameters: any()
        }
      ]
    }
  }
  fixedParameters: {
    booster: 'string'
    boostingType: 'string'
    growPolicy: 'string'
    learningRate: int
    maxBin: int
    maxDepth: int
    maxLeaves: int
    minDataInLeaf: int
    minSplitGain: int
    modelName: 'string'
    nEstimators: int
    numLeaves: int
    preprocessorName: 'string'
    regAlpha: int
    regLambda: int
    subsample: int
    subsampleFreq: int
    treeMethod: 'string'
    withMean: bool
    withStd: bool
  }
  limitSettings: {
    enableEarlyTermination: bool
    exitScore: int
    maxConcurrentTrials: int
    maxCoresPerTrial: int
    maxNodes: int
    maxTrials: int
    sweepConcurrentTrials: int
    sweepTrials: int
    timeout: 'string'
    trialTimeout: 'string'
  }
  nCrossValidations: {
    mode: 'string'
    // For remaining properties, see NCrossValidations objects
  }
  primaryMetric: 'string'
  searchSpace: [
    {
      booster: 'string'
      boostingType: 'string'
      growPolicy: 'string'
      learningRate: 'string'
      maxBin: 'string'
      maxDepth: 'string'
      maxLeaves: 'string'
      minDataInLeaf: 'string'
      minSplitGain: 'string'
      modelName: 'string'
      nEstimators: 'string'
      numLeaves: 'string'
      preprocessorName: 'string'
      regAlpha: 'string'
      regLambda: 'string'
      subsample: 'string'
      subsampleFreq: 'string'
      treeMethod: 'string'
      withMean: 'string'
      withStd: 'string'
    }
  ]
  sweepSettings: {
    earlyTermination: {
      delayEvaluation: int
      evaluationInterval: int
      policyType: 'string'
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm: 'string'
  }
  testData: {
    description: 'string'
    jobInputType: 'string'
    mode: 'string'
    uri: 'string'
  }
  testDataSize: int
  trainingSettings: {
    allowedTrainingAlgorithms: [
      'string'
    ]
    blockedTrainingAlgorithms: [
      'string'
    ]
    enableDnnTraining: bool
    enableModelExplainability: bool
    enableOnnxCompatibleModels: bool
    enableStackEnsemble: bool
    enableVoteEnsemble: bool
    ensembleModelDownloadTimeout: 'string'
    stackEnsembleSettings: {
      stackMetaLearnerKWargs: any()
      stackMetaLearnerTrainPercentage: int
      stackMetaLearnerType: 'string'
    }
    trainingMode: 'string'
  }
  validationData: {
    description: 'string'
    jobInputType: 'string'
    mode: 'string'
    uri: 'string'
  }
  validationDataSize: int
  weightColumnName: 'string'

For TextClassification, use:

  taskType: 'TextClassification'
  featurizationSettings: {
    datasetLanguage: 'string'
  }
  fixedParameters: {
    gradientAccumulationSteps: int
    learningRate: int
    learningRateScheduler: 'string'
    modelName: 'string'
    numberOfEpochs: int
    trainingBatchSize: int
    validationBatchSize: int
    warmupRatio: int
    weightDecay: int
  }
  limitSettings: {
    maxConcurrentTrials: int
    maxNodes: int
    maxTrials: int
    timeout: 'string'
    trialTimeout: 'string'
  }
  primaryMetric: 'string'
  searchSpace: [
    {
      gradientAccumulationSteps: 'string'
      learningRate: 'string'
      learningRateScheduler: 'string'
      modelName: 'string'
      numberOfEpochs: 'string'
      trainingBatchSize: 'string'
      validationBatchSize: 'string'
      warmupRatio: 'string'
      weightDecay: 'string'
    }
  ]
  sweepSettings: {
    earlyTermination: {
      delayEvaluation: int
      evaluationInterval: int
      policyType: 'string'
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm: 'string'
  }
  validationData: {
    description: 'string'
    jobInputType: 'string'
    mode: 'string'
    uri: 'string'
  }

For TextClassificationMultilabel, use:

  taskType: 'TextClassificationMultilabel'
  featurizationSettings: {
    datasetLanguage: 'string'
  }
  fixedParameters: {
    gradientAccumulationSteps: int
    learningRate: int
    learningRateScheduler: 'string'
    modelName: 'string'
    numberOfEpochs: int
    trainingBatchSize: int
    validationBatchSize: int
    warmupRatio: int
    weightDecay: int
  }
  limitSettings: {
    maxConcurrentTrials: int
    maxNodes: int
    maxTrials: int
    timeout: 'string'
    trialTimeout: 'string'
  }
  searchSpace: [
    {
      gradientAccumulationSteps: 'string'
      learningRate: 'string'
      learningRateScheduler: 'string'
      modelName: 'string'
      numberOfEpochs: 'string'
      trainingBatchSize: 'string'
      validationBatchSize: 'string'
      warmupRatio: 'string'
      weightDecay: 'string'
    }
  ]
  sweepSettings: {
    earlyTermination: {
      delayEvaluation: int
      evaluationInterval: int
      policyType: 'string'
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm: 'string'
  }
  validationData: {
    description: 'string'
    jobInputType: 'string'
    mode: 'string'
    uri: 'string'
  }

For TextNER, use:

  taskType: 'TextNER'
  featurizationSettings: {
    datasetLanguage: 'string'
  }
  fixedParameters: {
    gradientAccumulationSteps: int
    learningRate: int
    learningRateScheduler: 'string'
    modelName: 'string'
    numberOfEpochs: int
    trainingBatchSize: int
    validationBatchSize: int
    warmupRatio: int
    weightDecay: int
  }
  limitSettings: {
    maxConcurrentTrials: int
    maxNodes: int
    maxTrials: int
    timeout: 'string'
    trialTimeout: 'string'
  }
  searchSpace: [
    {
      gradientAccumulationSteps: 'string'
      learningRate: 'string'
      learningRateScheduler: 'string'
      modelName: 'string'
      numberOfEpochs: 'string'
      trainingBatchSize: 'string'
      validationBatchSize: 'string'
      warmupRatio: 'string'
      weightDecay: 'string'
    }
  ]
  sweepSettings: {
    earlyTermination: {
      delayEvaluation: int
      evaluationInterval: int
      policyType: 'string'
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm: 'string'
  }
  validationData: {
    description: 'string'
    jobInputType: 'string'
    mode: 'string'
    uri: 'string'
  }

NCrossValidations objects

Set the mode property to specify the type of object.

For Auto, use:

  mode: 'Auto'

For Custom, use:

  mode: 'Custom'
  value: int

EarlyTerminationPolicy objects

Set the policyType property to specify the type of object.

For Bandit, use:

  policyType: 'Bandit'
  slackAmount: int
  slackFactor: int

For MedianStopping, use:

  policyType: 'MedianStopping'

For TruncationSelection, use:

  policyType: 'TruncationSelection'
  truncationPercentage: int

ForecastHorizon objects

Set the mode property to specify the type of object.

For Auto, use:

  mode: 'Auto'

For Custom, use:

  mode: 'Custom'
  value: int

Seasonality objects

Set the mode property to specify the type of object.

For Auto, use:

  mode: 'Auto'

For Custom, use:

  mode: 'Custom'
  value: int

TargetLags objects

Set the mode property to specify the type of object.

For Auto, use:

  mode: 'Auto'

For Custom, use:

  mode: 'Custom'
  values: [
    int
  ]

TargetRollingWindowSize objects

Set the mode property to specify the type of object.

For Auto, use:

  mode: 'Auto'

For Custom, use:

  mode: 'Custom'
  value: int

DistributionConfiguration objects

Set the distributionType property to specify the type of object.

For Mpi, use:

  distributionType: 'Mpi'
  processCountPerInstance: int

For PyTorch, use:

  distributionType: 'PyTorch'
  processCountPerInstance: int

For Ray, use:

  distributionType: 'Ray'
  address: 'string'
  dashboardPort: int
  headNodeAdditionalArgs: 'string'
  includeDashboard: bool
  port: int
  workerNodeAdditionalArgs: 'string'

For TensorFlow, use:

  distributionType: 'TensorFlow'
  parameterServerCount: int
  workerCount: int

JobInput objects

Set the jobInputType property to specify the type of object.

For custom_model, use:

  jobInputType: 'custom_model'
  mode: 'string'
  uri: 'string'

For literal, use:

  jobInputType: 'literal'
  value: 'string'

For mlflow_model, use:

  jobInputType: 'mlflow_model'
  mode: 'string'
  uri: 'string'

For mltable, use:

  jobInputType: 'mltable'
  mode: 'string'
  uri: 'string'

For triton_model, use:

  jobInputType: 'triton_model'
  mode: 'string'
  uri: 'string'

For uri_file, use:

  jobInputType: 'uri_file'
  mode: 'string'
  uri: 'string'

For uri_folder, use:

  jobInputType: 'uri_folder'
  mode: 'string'
  uri: 'string'

LabelingJobMediaProperties objects

Set the mediaType property to specify the type of object.

For Image, use:

  mediaType: 'Image'
  annotationType: 'string'

For Text, use:

  mediaType: 'Text'
  annotationType: 'string'

MLAssistConfiguration objects

Set the mlAssist property to specify the type of object.

For Disabled, use:

  mlAssist: 'Disabled'

For Enabled, use:

  mlAssist: 'Enabled'
  inferencingComputeBinding: 'string'
  trainingComputeBinding: 'string'

SparkJobEntry objects

Set the sparkJobEntryType property to specify the type of object.

For SparkJobPythonEntry, use:

  sparkJobEntryType: 'SparkJobPythonEntry'
  file: 'string'

For SparkJobScalaEntry, use:

  sparkJobEntryType: 'SparkJobScalaEntry'
  className: 'string'

SamplingAlgorithm objects

Set the samplingAlgorithmType property to specify the type of object.

For Bayesian, use:

  samplingAlgorithmType: 'Bayesian'

For Grid, use:

  samplingAlgorithmType: 'Grid'

For Random, use:

  samplingAlgorithmType: 'Random'
  logbase: 'string'
  rule: 'string'
  seed: int

MonitoringAlertNotificationSettingsBase objects

Set the alertNotificationType property to specify the type of object.

For AzureMonitor, use:

  alertNotificationType: 'AzureMonitor'

For Email, use:

  alertNotificationType: 'Email'
  emailNotificationSetting: {
    emailOn: [
      'string'
    ]
    emails: [
      'string'
    ]
    webhooks: {
      {customized property}: {
        eventType: 'string'
        webhookType: 'string'
        // For remaining properties, see Webhook objects
      }
    }
  }

MonitoringSignalBase objects

Set the signalType property to specify the type of object.

For Custom, use:

  signalType: 'Custom'
  componentId: 'string'
  inputAssets: {
    {customized property}: {
      asset: any()
      dataContext: 'string'
      preprocessingComponentId: 'string'
      targetColumnName: 'string'
    }
  }
  metricThresholds: [
    {
      metric: 'string'
      threshold: {
        value: int
      }
    }
  ]

For DataDrift, use:

  signalType: 'DataDrift'
  baselineData: {
    asset: any()
    dataContext: 'string'
    preprocessingComponentId: 'string'
    targetColumnName: 'string'
  }
  dataSegment: {
    feature: 'string'
    values: [
      'string'
    ]
  }
  features: {
    filterType: 'string'
    // For remaining properties, see MonitoringFeatureFilterBase objects
  }
  metricThresholds: [
    {
      threshold: {
        value: int
      }
      dataType: 'string'
      // For remaining properties, see DataDriftMetricThresholdBase objects
    }
  ]
  targetData: {
    asset: any()
    dataContext: 'string'
    preprocessingComponentId: 'string'
    targetColumnName: 'string'
  }

For DataQuality, use:

  signalType: 'DataQuality'
  baselineData: {
    asset: any()
    dataContext: 'string'
    preprocessingComponentId: 'string'
    targetColumnName: 'string'
  }
  features: {
    filterType: 'string'
    // For remaining properties, see MonitoringFeatureFilterBase objects
  }
  metricThresholds: [
    {
      threshold: {
        value: int
      }
      dataType: 'string'
      // For remaining properties, see DataQualityMetricThresholdBase objects
    }
  ]
  targetData: {
    asset: any()
    dataContext: 'string'
    preprocessingComponentId: 'string'
    targetColumnName: 'string'
  }

For FeatureAttributionDrift, use:

  signalType: 'FeatureAttributionDrift'
  baselineData: {
    asset: any()
    dataContext: 'string'
    preprocessingComponentId: 'string'
    targetColumnName: 'string'
  }
  metricThreshold: {
    metric: 'NormalizedDiscountedCumulativeGain'
    threshold: {
      value: int
    }
  }
  modelType: 'string'
  targetData: {
    asset: any()
    dataContext: 'string'
    preprocessingComponentId: 'string'
    targetColumnName: 'string'
  }

For ModelPerformance, use:

  signalType: 'ModelPerformance'
  baselineData: {
    asset: any()
    dataContext: 'string'
    preprocessingComponentId: 'string'
    targetColumnName: 'string'
  }
  dataSegment: {
    feature: 'string'
    values: [
      'string'
    ]
  }
  metricThreshold: {
    threshold: {
      value: int
    }
    modelType: 'string'
    // For remaining properties, see ModelPerformanceMetricThresholdBase objects
  }
  targetData: {
    asset: any()
    dataContext: 'string'
    preprocessingComponentId: 'string'
    targetColumnName: 'string'
  }

For PredictionDrift, use:

  signalType: 'PredictionDrift'
  baselineData: {
    asset: any()
    dataContext: 'string'
    preprocessingComponentId: 'string'
    targetColumnName: 'string'
  }
  metricThresholds: [
    {
      threshold: {
        value: int
      }
      dataType: 'string'
      // For remaining properties, see PredictionDriftMetricThresholdBase objects
    }
  ]
  modelType: 'string'
  targetData: {
    asset: any()
    dataContext: 'string'
    preprocessingComponentId: 'string'
    targetColumnName: 'string'
  }

MonitoringFeatureFilterBase objects

Set the filterType property to specify the type of object.

For AllFeatures, use:

  filterType: 'AllFeatures'

For FeatureSubset, use:

  filterType: 'FeatureSubset'
  features: [
    'string'
  ]

For TopNByAttribution, use:

  filterType: 'TopNByAttribution'
  top: int

DataDriftMetricThresholdBase objects

Set the dataType property to specify the type of object.

For Categorical, use:

  dataType: 'Categorical'
  metric: 'string'

For Numerical, use:

  dataType: 'Numerical'
  metric: 'string'

DataQualityMetricThresholdBase objects

Set the dataType property to specify the type of object.

For Categorical, use:

  dataType: 'Categorical'
  metric: 'string'

For Numerical, use:

  dataType: 'Numerical'
  metric: 'string'

ModelPerformanceMetricThresholdBase objects

Set the modelType property to specify the type of object.

For Classification, use:

  modelType: 'Classification'
  metric: 'string'

For Regression, use:

  modelType: 'Regression'
  metric: 'string'

PredictionDriftMetricThresholdBase objects

Set the dataType property to specify the type of object.

For Categorical, use:

  dataType: 'Categorical'
  metric: 'string'

For Numerical, use:

  dataType: 'Numerical'
  metric: 'string'

DataImportSource objects

Set the sourceType property to specify the type of object.

For database, use:

  sourceType: 'database'
  query: 'string'
  storedProcedure: 'string'
  storedProcedureParams: [
    {
      {customized property}: 'string'
    }
  ]
  tableName: 'string'

For file_system, use:

  sourceType: 'file_system'
  path: 'string'

TriggerBase objects

Set the triggerType property to specify the type of object.

For Cron, use:

  triggerType: 'Cron'
  expression: 'string'

For Recurrence, use:

  triggerType: 'Recurrence'
  frequency: 'string'
  interval: int
  schedule: {
    hours: [
      int
    ]
    minutes: [
      int
    ]
    monthDays: [
      int
    ]
    weekDays: [
      'string'
    ]
  }

Property values

workspaces/schedules

Name Description Value
name The resource name

See how to set names and types for child resources in Bicep.		 string (required)
parent In Bicep, you can specify the parent resource for a child resource. You only need to add this property when the child resource is declared outside of the parent resource.

For more information, see Child resource outside parent resource.		 Symbolic name for resource of type: workspaces
properties [Required] Additional attributes of the entity. ScheduleProperties (required)

ScheduleProperties

Name Description Value
action [Required] Specifies the action of the schedule ScheduleActionBase (required)
description The asset description text. string
displayName Display name of schedule. string
isEnabled Is the schedule enabled? bool
properties The asset property dictionary. ResourceBaseProperties
tags Tag dictionary. Tags can be added, removed, and updated. object
trigger [Required] Specifies the trigger details TriggerBase (required)

ScheduleActionBase

Name Description Value
actionType Set the object type CreateJob
CreateMonitor
ImportData
InvokeBatchEndpoint (required)

JobScheduleAction

Name Description Value
actionType [Required] Specifies the action type of the schedule 'CreateJob' (required)
jobDefinition [Required] Defines Schedule action definition details. JobBaseProperties (required)

JobBaseProperties

Name Description Value
componentId ARM resource ID of the component resource. string
computeId ARM resource ID of the compute resource. string
description The asset description text. string
displayName Display name of job. string
experimentName The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment. string
identity Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null.
Defaults to AmlToken if null.		 IdentityConfiguration
isArchived Is the asset archived? bool
notificationSetting Notification setting for the job NotificationSetting
properties The asset property dictionary. ResourceBaseProperties
secretsConfiguration Configuration for secrets to be made available during runtime. JobBaseSecretsConfiguration
services List of JobEndpoints.
For local jobs, a job endpoint will have an endpoint value of FileStreamObject.		 JobBaseServices
tags Tag dictionary. Tags can be added, removed, and updated. object
jobType Set the object type AutoML
Command
Labeling
Pipeline
Spark
Sweep (required)

IdentityConfiguration

Name Description Value
identityType Set the object type AMLToken
Managed
UserIdentity (required)

AmlToken

Name Description Value
identityType [Required] Specifies the type of identity framework. 'AMLToken' (required)

ManagedIdentity

Name Description Value
identityType [Required] Specifies the type of identity framework. 'Managed' (required)
clientId Specifies a user-assigned identity by client ID. For system-assigned, do not set this field. string

Constraints:
Min length = 36
Max length = 36
Pattern = ^[0-9a-fA-F]{8}-([0-9a-fA-F]{4}-){3}[0-9a-fA-F]{12}$
objectId Specifies a user-assigned identity by object ID. For system-assigned, do not set this field. string

Constraints:
Min length = 36
Max length = 36
Pattern = ^[0-9a-fA-F]{8}-([0-9a-fA-F]{4}-){3}[0-9a-fA-F]{12}$
resourceId Specifies a user-assigned identity by ARM resource ID. For system-assigned, do not set this field. string

UserIdentity

Name Description Value
identityType [Required] Specifies the type of identity framework. 'UserIdentity' (required)

NotificationSetting

Name Description Value
emailOn Send email notification to user on specified notification type String array containing any of:
'JobCancelled'
'JobCompleted'
'JobFailed'
emails This is the email recipient list which has a limitation of 499 characters in total concat with comma separator string[]
webhooks Send webhook callback to a service. Key is a user-provided name for the webhook. NotificationSettingWebhooks

NotificationSettingWebhooks

Name Description Value
{customized property} Webhook

Webhook

Name Description Value
eventType Send callback on a specified notification event string
webhookType Set the object type AzureDevOps (required)

AzureDevOpsWebhook

Name Description Value
webhookType [Required] Specifies the type of service to send a callback 'AzureDevOps' (required)

ResourceBaseProperties

Name Description Value
{customized property} string

JobBaseSecretsConfiguration

Name Description Value
{customized property} SecretConfiguration

SecretConfiguration

Name Description Value
uri Secret Uri.
Sample Uri : https://myvault.vault.azure.net/secrets/mysecretname/secretversion		 string
workspaceSecretName Name of secret in workspace key vault. string

JobBaseServices

Name Description Value
{customized property} JobService

JobService

Name Description Value
endpoint Url for endpoint. string
jobServiceType Endpoint type. string
nodes Nodes that user would like to start the service on.
If Nodes is not set or set to null, the service will only be started on leader node.		 Nodes
port Port for endpoint set by user. int
properties Additional properties to set on the endpoint. JobServiceProperties

Nodes

Name Description Value
nodesValueType Set the object type All (required)

AllNodes

Name Description Value
nodesValueType [Required] Type of the Nodes value 'All' (required)

JobServiceProperties

Name Description Value
{customized property} string

AutoMLJob

Name Description Value
jobType [Required] Specifies the type of job. 'AutoML' (required)
environmentId The ARM resource ID of the Environment specification for the job.
This is optional value to provide, if not provided, AutoML will default this to Production AutoML curated environment version when running the job.		 string
environmentVariables Environment variables included in the job. AutoMLJobEnvironmentVariables
outputs Mapping of output data bindings used in the job. AutoMLJobOutputs
queueSettings Queue settings for the job QueueSettings
resources Compute Resource configuration for the job. JobResourceConfiguration
taskDetails [Required] This represents scenario which can be one of Tables/NLP/Image AutoMLVertical (required)

AutoMLJobEnvironmentVariables

Name Description Value
{customized property} string

AutoMLJobOutputs

Name Description Value
{customized property} JobOutput

JobOutput

Name Description Value
description Description for the output. string
jobOutputType Set the object type custom_model
mlflow_model
mltable
triton_model
uri_file
uri_folder (required)

CustomModelJobOutput

Name Description Value
jobOutputType [Required] Specifies the type of job. 'custom_model' (required)
assetName Output Asset Name. string
assetVersion Output Asset Version. string
autoDeleteSetting Auto delete setting of output data asset. AutoDeleteSetting
mode Output Asset Delivery Mode. 'Direct'
'ReadWriteMount'
'Upload'
uri Output Asset URI. string

AutoDeleteSetting

Name Description Value
condition When to check if an asset is expired 'CreatedGreaterThan'
'LastAccessedGreaterThan'
value Expiration condition value. string

MLFlowModelJobOutput

Name Description Value
jobOutputType [Required] Specifies the type of job. 'mlflow_model' (required)
assetName Output Asset Name. string
assetVersion Output Asset Version. string
autoDeleteSetting Auto delete setting of output data asset. AutoDeleteSetting
mode Output Asset Delivery Mode. 'Direct'
'ReadWriteMount'
'Upload'
uri Output Asset URI. string

MLTableJobOutput

Name Description Value
jobOutputType [Required] Specifies the type of job. 'mltable' (required)
assetName Output Asset Name. string
assetVersion Output Asset Version. string
autoDeleteSetting Auto delete setting of output data asset. AutoDeleteSetting
mode Output Asset Delivery Mode. 'Direct'
'ReadWriteMount'
'Upload'
uri Output Asset URI. string

TritonModelJobOutput

Name Description Value
jobOutputType [Required] Specifies the type of job. 'triton_model' (required)
assetName Output Asset Name. string
assetVersion Output Asset Version. string
autoDeleteSetting Auto delete setting of output data asset. AutoDeleteSetting
mode Output Asset Delivery Mode. 'Direct'
'ReadWriteMount'
'Upload'
uri Output Asset URI. string

UriFileJobOutput

Name Description Value
jobOutputType [Required] Specifies the type of job. 'uri_file' (required)
assetName Output Asset Name. string
assetVersion Output Asset Version. string
autoDeleteSetting Auto delete setting of output data asset. AutoDeleteSetting
mode Output Asset Delivery Mode. 'Direct'
'ReadWriteMount'
'Upload'
uri Output Asset URI. string

UriFolderJobOutput

Name Description Value
jobOutputType [Required] Specifies the type of job. 'uri_folder' (required)
assetName Output Asset Name. string
assetVersion Output Asset Version. string
autoDeleteSetting Auto delete setting of output data asset. AutoDeleteSetting
mode Output Asset Delivery Mode. 'Direct'
'ReadWriteMount'
'Upload'
uri Output Asset URI. string

QueueSettings

Name Description Value
jobTier Enum to determine the job tier. 'Basic'
'Premium'
'Spot'
'Standard'
priority Controls the priority of the job on a compute. int

JobResourceConfiguration

Name Description Value
dockerArgs Extra arguments to pass to the Docker run command. This would override any parameters that have already been set by the system, or in this section. This parameter is only supported for Azure ML compute types. string
instanceCount Optional number of instances or nodes used by the compute target. int
instanceType Optional type of VM used as supported by the compute target. string
locations Locations where the job can run. string[]
maxInstanceCount Optional max allowed number of instances or nodes to be used by the compute target.
For use with elastic training, currently supported by PyTorch distribution type only.		 int
properties Additional properties bag. ResourceConfigurationProperties
shmSize Size of the docker container's shared memory block. This should be in the format of (number)(unit) where number as to be greater than 0 and the unit can be one of b(bytes), k(kilobytes), m(megabytes), or g(gigabytes). string

Constraints:
Pattern = \d+[bBkKmMgG]

ResourceConfigurationProperties

Name Description Value
{customized property} For Bicep, you can use the any() function.

AutoMLVertical

Name Description Value
logVerbosity Log verbosity for the job. 'Critical'
'Debug'
'Error'
'Info'
'NotSet'
'Warning'
targetColumnName Target column name: This is prediction values column.
Also known as label column name in context of classification tasks.		 string
trainingData [Required] Training data input. MLTableJobInput (required)
taskType Set the object type Classification
Forecasting
ImageClassification
ImageClassificationMultilabel
ImageInstanceSegmentation
ImageObjectDetection
Regression
TextClassification
TextClassificationMultilabel
TextNER (required)

MLTableJobInput

Name Description Value
description Description for the input. string
jobInputType [Required] Specifies the type of job. 'custom_model'
'literal'
'mlflow_model'
'mltable'
'triton_model'
'uri_file'
'uri_folder' (required)
mode Input Asset Delivery Mode. 'Direct'
'Download'
'EvalDownload'
'EvalMount'
'ReadOnlyMount'
'ReadWriteMount'
uri [Required] Input Asset URI. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]

Classification

Name Description Value
taskType [Required] Task type for AutoMLJob. 'Classification' (required)
cvSplitColumnNames Columns to use for CVSplit data. string[]
featurizationSettings Featurization inputs needed for AutoML job. TableVerticalFeaturizationSettings
fixedParameters Model/training parameters that will remain constant throughout training. TableFixedParameters
limitSettings Execution constraints for AutoMLJob. TableVerticalLimitSettings
nCrossValidations Number of cross validation folds to be applied on training dataset
when validation dataset is not provided.		 NCrossValidations
positiveLabel Positive label for binary metrics calculation. string
primaryMetric Primary metric for the task. 'AUCWeighted'
'Accuracy'
'AveragePrecisionScoreWeighted'
'NormMacroRecall'
'PrecisionScoreWeighted'
searchSpace Search space for sampling different combinations of models and their hyperparameters. TableParameterSubspace[]
sweepSettings Settings for model sweeping and hyperparameter tuning. TableSweepSettings
testData Test data input. MLTableJobInput
testDataSize The fraction of test dataset that needs to be set aside for validation purpose.
Values between (0.0 , 1.0)
Applied when validation dataset is not provided.		 int
trainingSettings Inputs for training phase for an AutoML Job. ClassificationTrainingSettings
validationData Validation data inputs. MLTableJobInput
validationDataSize The fraction of training dataset that needs to be set aside for validation purpose.
Values between (0.0 , 1.0)
Applied when validation dataset is not provided.		 int
weightColumnName The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down. string

TableVerticalFeaturizationSettings

Name Description Value
blockedTransformers These transformers shall not be used in featurization. String array containing any of:
'CatTargetEncoder'
'CountVectorizer'
'HashOneHotEncoder'
'LabelEncoder'
'NaiveBayes'
'OneHotEncoder'
'TextTargetEncoder'
'TfIdf'
'WoETargetEncoder'
'WordEmbedding'
columnNameAndTypes Dictionary of column name and its type (int, float, string, datetime etc). TableVerticalFeaturizationSettingsColumnNameAndTypes
datasetLanguage Dataset language, useful for the text data. string
enableDnnFeaturization Determines whether to use Dnn based featurizers for data featurization. bool
mode Featurization mode - User can keep the default 'Auto' mode and AutoML will take care of necessary transformation of the data in featurization phase.
If 'Off' is selected then no featurization is done.
If 'Custom' is selected then user can specify additional inputs to customize how featurization is done.		 'Auto'
'Custom'
'Off'
transformerParams User can specify additional transformers to be used along with the columns to which it would be applied and parameters for the transformer constructor. TableVerticalFeaturizationSettingsTransformerParams

TableVerticalFeaturizationSettingsColumnNameAndTypes

Name Description Value
{customized property} string

TableVerticalFeaturizationSettingsTransformerParams

Name Description Value
{customized property} ColumnTransformer[]

ColumnTransformer

Name Description Value
fields Fields to apply transformer logic on. string[]
parameters Different properties to be passed to transformer.
Input expected is dictionary of key,value pairs in JSON format.
 For Bicep, you can use the any() function.

TableFixedParameters

Name Description Value
booster Specify the boosting type, e.g gbdt for XGBoost. string
boostingType Specify the boosting type, e.g gbdt for LightGBM. string
growPolicy Specify the grow policy, which controls the way new nodes are added to the tree. string
learningRate The learning rate for the training procedure. int
maxBin Specify the Maximum number of discrete bins to bucket continuous features . int
maxDepth Specify the max depth to limit the tree depth explicitly. int
maxLeaves Specify the max leaves to limit the tree leaves explicitly. int
minDataInLeaf The minimum number of data per leaf. int
minSplitGain Minimum loss reduction required to make a further partition on a leaf node of the tree. int
modelName The name of the model to train. string
nEstimators Specify the number of trees (or rounds) in an model. int
numLeaves Specify the number of leaves. int
preprocessorName The name of the preprocessor to use. string
regAlpha L1 regularization term on weights. int
regLambda L2 regularization term on weights. int
subsample Subsample ratio of the training instance. int
subsampleFreq Frequency of subsample. int
treeMethod Specify the tree method. string
withMean If true, center before scaling the data with StandardScalar. bool
withStd If true, scaling the data with Unit Variance with StandardScalar. bool

TableVerticalLimitSettings

Name Description Value
enableEarlyTermination Enable early termination, determines whether or not if AutoMLJob will terminate early if there is no score improvement in last 20 iterations. bool
exitScore Exit score for the AutoML job. int
maxConcurrentTrials Maximum Concurrent iterations. int
maxCoresPerTrial Max cores per iteration. int
maxNodes Maximum nodes to use for the experiment. int
maxTrials Number of iterations. int
sweepConcurrentTrials Number of concurrent sweeping runs that user wants to trigger. int
sweepTrials Number of sweeping runs that user wants to trigger. int
timeout AutoML job timeout. string
trialTimeout Iteration timeout. string

NCrossValidations

Name Description Value
mode Set the object type Auto
Custom (required)

AutoNCrossValidations

Name Description Value
mode [Required] Mode for determining N-Cross validations. 'Auto' (required)

CustomNCrossValidations

Name Description Value
mode [Required] Mode for determining N-Cross validations. 'Custom' (required)
value [Required] N-Cross validations value. int (required)

TableParameterSubspace

Name Description Value
booster Specify the boosting type, e.g gbdt for XGBoost. string
boostingType Specify the boosting type, e.g gbdt for LightGBM. string
growPolicy Specify the grow policy, which controls the way new nodes are added to the tree. string
learningRate The learning rate for the training procedure. string
maxBin Specify the Maximum number of discrete bins to bucket continuous features . string
maxDepth Specify the max depth to limit the tree depth explicitly. string
maxLeaves Specify the max leaves to limit the tree leaves explicitly. string
minDataInLeaf The minimum number of data per leaf. string
minSplitGain Minimum loss reduction required to make a further partition on a leaf node of the tree. string
modelName The name of the model to train. string
nEstimators Specify the number of trees (or rounds) in an model. string
numLeaves Specify the number of leaves. string
preprocessorName The name of the preprocessor to use. string
regAlpha L1 regularization term on weights. string
regLambda L2 regularization term on weights. string
subsample Subsample ratio of the training instance. string
subsampleFreq Frequency of subsample string
treeMethod Specify the tree method. string
withMean If true, center before scaling the data with StandardScalar. string
withStd If true, scaling the data with Unit Variance with StandardScalar. string

TableSweepSettings

Name Description Value
earlyTermination Type of early termination policy for the sweeping job. EarlyTerminationPolicy
samplingAlgorithm [Required] Type of sampling algorithm. 'Bayesian'
'Grid'
'Random' (required)

EarlyTerminationPolicy

Name Description Value
delayEvaluation Number of intervals by which to delay the first evaluation. int
evaluationInterval Interval (number of runs) between policy evaluations. int
policyType Set the object type Bandit
MedianStopping
TruncationSelection (required)

BanditPolicy

Name Description Value
policyType [Required] Name of policy configuration 'Bandit' (required)
slackAmount Absolute distance allowed from the best performing run. int
slackFactor Ratio of the allowed distance from the best performing run. int

MedianStoppingPolicy

Name Description Value
policyType [Required] Name of policy configuration 'MedianStopping' (required)

TruncationSelectionPolicy

Name Description Value
policyType [Required] Name of policy configuration 'TruncationSelection' (required)
truncationPercentage The percentage of runs to cancel at each evaluation interval. int

ClassificationTrainingSettings

Name Description Value
allowedTrainingAlgorithms Allowed models for classification task. String array containing any of:
'BernoulliNaiveBayes'
'DecisionTree'
'ExtremeRandomTrees'
'GradientBoosting'
'KNN'
'LightGBM'
'LinearSVM'
'LogisticRegression'
'MultinomialNaiveBayes'
'RandomForest'
'SGD'
'SVM'
'XGBoostClassifier'
blockedTrainingAlgorithms Blocked models for classification task. String array containing any of:
'BernoulliNaiveBayes'
'DecisionTree'
'ExtremeRandomTrees'
'GradientBoosting'
'KNN'
'LightGBM'
'LinearSVM'
'LogisticRegression'
'MultinomialNaiveBayes'
'RandomForest'
'SGD'
'SVM'
'XGBoostClassifier'
enableDnnTraining Enable recommendation of DNN models. bool
enableModelExplainability Flag to turn on explainability on best model. bool
enableOnnxCompatibleModels Flag for enabling onnx compatible models. bool
enableStackEnsemble Enable stack ensemble run. bool
enableVoteEnsemble Enable voting ensemble run. bool
ensembleModelDownloadTimeout During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded.
Configure this parameter with a higher value than 300 secs, if more time is needed.		 string
stackEnsembleSettings Stack ensemble settings for stack ensemble run. StackEnsembleSettings
trainingMode TrainingMode mode - Setting to 'auto' is same as setting it to 'non-distributed' for now, however in the future may result in mixed mode or heuristics based mode selection. Default is 'auto'.
If 'Distributed' then only distributed featurization is used and distributed algorithms are chosen.
If 'NonDistributed' then only non distributed algorithms are chosen.
 'Auto'
'Distributed'
'NonDistributed'

StackEnsembleSettings

Name Description Value
stackMetaLearnerKWargs Optional parameters to pass to the initializer of the meta-learner. For Bicep, you can use the any() function.
stackMetaLearnerTrainPercentage Specifies the proportion of the training set (when choosing train and validation type of training) to be reserved for training the meta-learner. Default value is 0.2. int
stackMetaLearnerType The meta-learner is a model trained on the output of the individual heterogeneous models. 'ElasticNet'
'ElasticNetCV'
'LightGBMClassifier'
'LightGBMRegressor'
'LinearRegression'
'LogisticRegression'
'LogisticRegressionCV'
'None'

Forecasting

Name Description Value
taskType [Required] Task type for AutoMLJob. 'Forecasting' (required)
cvSplitColumnNames Columns to use for CVSplit data. string[]
featurizationSettings Featurization inputs needed for AutoML job. TableVerticalFeaturizationSettings
fixedParameters Model/training parameters that will remain constant throughout training. TableFixedParameters
forecastingSettings Forecasting task specific inputs. ForecastingSettings
limitSettings Execution constraints for AutoMLJob. TableVerticalLimitSettings
nCrossValidations Number of cross validation folds to be applied on training dataset
when validation dataset is not provided.		 NCrossValidations
primaryMetric Primary metric for forecasting task. 'NormalizedMeanAbsoluteError'
'NormalizedRootMeanSquaredError'
'R2Score'
'SpearmanCorrelation'
searchSpace Search space for sampling different combinations of models and their hyperparameters. TableParameterSubspace[]
sweepSettings Settings for model sweeping and hyperparameter tuning. TableSweepSettings
testData Test data input. MLTableJobInput
testDataSize The fraction of test dataset that needs to be set aside for validation purpose.
Values between (0.0 , 1.0)
Applied when validation dataset is not provided.		 int
trainingSettings Inputs for training phase for an AutoML Job. ForecastingTrainingSettings
validationData Validation data inputs. MLTableJobInput
validationDataSize The fraction of training dataset that needs to be set aside for validation purpose.
Values between (0.0 , 1.0)
Applied when validation dataset is not provided.		 int
weightColumnName The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down. string

ForecastingSettings

Name Description Value
countryOrRegionForHolidays Country or region for holidays for forecasting tasks.
These should be ISO 3166 two-letter country/region codes, for example 'US' or 'GB'.		 string
cvStepSize Number of periods between the origin time of one CV fold and the next fold. For
example, if CVStepSize = 3 for daily data, the origin time for each fold will be
three days apart.		 int
featureLags Flag for generating lags for the numeric features with 'auto' or null. 'Auto'
'None'
featuresUnknownAtForecastTime The feature columns that are available for training but unknown at the time of forecast/inference.
If features_unknown_at_forecast_time is not set, it is assumed that all the feature columns in the dataset are known at inference time.		 string[]
forecastHorizon The desired maximum forecast horizon in units of time-series frequency. ForecastHorizon
frequency When forecasting, this parameter represents the period with which the forecast is desired, for example daily, weekly, yearly, etc. The forecast frequency is dataset frequency by default. string
seasonality Set time series seasonality as an integer multiple of the series frequency.
If seasonality is set to 'auto', it will be inferred.		 Seasonality
shortSeriesHandlingConfig The parameter defining how if AutoML should handle short time series. 'Auto'
'Drop'
'None'
'Pad'
targetAggregateFunction The function to be used to aggregate the time series target column to conform to a user specified frequency.
If the TargetAggregateFunction is set i.e. not 'None', but the freq parameter is not set, the error is raised. The possible target aggregation functions are: "sum", "max", "min" and "mean".		 'Max'
'Mean'
'Min'
'None'
'Sum'
targetLags The number of past periods to lag from the target column. TargetLags
targetRollingWindowSize The number of past periods used to create a rolling window average of the target column. TargetRollingWindowSize
timeColumnName The name of the time column. This parameter is required when forecasting to specify the datetime column in the input data used for building the time series and inferring its frequency. string
timeSeriesIdColumnNames The names of columns used to group a timeseries. It can be used to create multiple series.
If grain is not defined, the data set is assumed to be one time-series. This parameter is used with task type forecasting.		 string[]
useStl Configure STL Decomposition of the time-series target column. 'None'
'Season'
'SeasonTrend'

ForecastHorizon

Name Description Value
mode Set the object type Auto
Custom (required)

AutoForecastHorizon

Name Description Value
mode [Required] Set forecast horizon value selection mode. 'Auto' (required)

CustomForecastHorizon

Name Description Value
mode [Required] Set forecast horizon value selection mode. 'Custom' (required)
value [Required] Forecast horizon value. int (required)

Seasonality

Name Description Value
mode Set the object type Auto
Custom (required)

AutoSeasonality

Name Description Value
mode [Required] Seasonality mode. 'Auto' (required)

CustomSeasonality

Name Description Value
mode [Required] Seasonality mode. 'Custom' (required)
value [Required] Seasonality value. int (required)

TargetLags

Name Description Value
mode Set the object type Auto
Custom (required)

AutoTargetLags

Name Description Value
mode [Required] Set target lags mode - Auto/Custom 'Auto' (required)

CustomTargetLags

Name Description Value
mode [Required] Set target lags mode - Auto/Custom 'Custom' (required)
values [Required] Set target lags values. int[] (required)

TargetRollingWindowSize

Name Description Value
mode Set the object type Auto
Custom (required)

AutoTargetRollingWindowSize

Name Description Value
mode [Required] TargetRollingWindowSiz detection mode. 'Auto' (required)

CustomTargetRollingWindowSize

Name Description Value
mode [Required] TargetRollingWindowSiz detection mode. 'Custom' (required)
value [Required] TargetRollingWindowSize value. int (required)

ForecastingTrainingSettings

Name Description Value
allowedTrainingAlgorithms Allowed models for forecasting task. String array containing any of:
'Arimax'
'AutoArima'
'Average'
'DecisionTree'
'ElasticNet'
'ExponentialSmoothing'
'ExtremeRandomTrees'
'GradientBoosting'
'KNN'
'LassoLars'
'LightGBM'
'Naive'
'Prophet'
'RandomForest'
'SGD'
'SeasonalAverage'
'SeasonalNaive'
'TCNForecaster'
'XGBoostRegressor'
blockedTrainingAlgorithms Blocked models for forecasting task. String array containing any of:
'Arimax'
'AutoArima'
'Average'
'DecisionTree'
'ElasticNet'
'ExponentialSmoothing'
'ExtremeRandomTrees'
'GradientBoosting'
'KNN'
'LassoLars'
'LightGBM'
'Naive'
'Prophet'
'RandomForest'
'SGD'
'SeasonalAverage'
'SeasonalNaive'
'TCNForecaster'
'XGBoostRegressor'
enableDnnTraining Enable recommendation of DNN models. bool
enableModelExplainability Flag to turn on explainability on best model. bool
enableOnnxCompatibleModels Flag for enabling onnx compatible models. bool
enableStackEnsemble Enable stack ensemble run. bool
enableVoteEnsemble Enable voting ensemble run. bool
ensembleModelDownloadTimeout During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded.
Configure this parameter with a higher value than 300 secs, if more time is needed.		 string
stackEnsembleSettings Stack ensemble settings for stack ensemble run. StackEnsembleSettings
trainingMode TrainingMode mode - Setting to 'auto' is same as setting it to 'non-distributed' for now, however in the future may result in mixed mode or heuristics based mode selection. Default is 'auto'.
If 'Distributed' then only distributed featurization is used and distributed algorithms are chosen.
If 'NonDistributed' then only non distributed algorithms are chosen.
 'Auto'
'Distributed'
'NonDistributed'

ImageClassification

Name Description Value
taskType [Required] Task type for AutoMLJob. 'ImageClassification' (required)
limitSettings [Required] Limit settings for the AutoML job. ImageLimitSettings (required)
modelSettings Settings used for training the model. ImageModelSettingsClassification
primaryMetric Primary metric to optimize for this task. 'AUCWeighted'
'Accuracy'
'AveragePrecisionScoreWeighted'
'NormMacroRecall'
'PrecisionScoreWeighted'
searchSpace Search space for sampling different combinations of models and their hyperparameters. ImageModelDistributionSettingsClassification[]
sweepSettings Model sweeping and hyperparameter sweeping related settings. ImageSweepSettings
validationData Validation data inputs. MLTableJobInput
validationDataSize The fraction of training dataset that needs to be set aside for validation purpose.
Values between (0.0 , 1.0)
Applied when validation dataset is not provided.
 int

ImageLimitSettings

Name Description Value
maxConcurrentTrials Maximum number of concurrent AutoML iterations. int
maxTrials Maximum number of AutoML iterations. int
timeout AutoML job timeout. string

ImageModelSettingsClassification

Name Description Value
advancedSettings Settings for advanced scenarios. string
amsGradient Enable AMSGrad when optimizer is 'adam' or 'adamw'. bool
augmentations Settings for using Augmentations. string
beta1 Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. int
beta2 Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. int
checkpointFrequency Frequency to store model checkpoints. Must be a positive integer. int
checkpointModel The pretrained checkpoint model for incremental training. MLFlowModelJobInput
checkpointRunId The id of a previous run that has a pretrained checkpoint for incremental training. string
distributed Whether to use distributed training. bool
earlyStopping Enable early stopping logic during training. bool
earlyStoppingDelay Minimum number of epochs or validation evaluations to wait before primary metric improvement
is tracked for early stopping. Must be a positive integer.		 int
earlyStoppingPatience Minimum number of epochs or validation evaluations with no primary metric improvement before
the run is stopped. Must be a positive integer.		 int
enableOnnxNormalization Enable normalization when exporting ONNX model. bool
evaluationFrequency Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. int
gradientAccumulationStep Gradient accumulation means running a configured number of "GradAccumulationStep" steps without
updating the model weights while accumulating the gradients of those steps, and then using
the accumulated gradients to compute the weight updates. Must be a positive integer.		 int
layersToFreeze Number of layers to freeze for the model. Must be a positive integer.
For instance, passing 2 as value for 'seresnext' means
freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please
see: /azure/machine-learning/how-to-auto-train-image-models.		 int
learningRate Initial learning rate. Must be a float in the range [0, 1]. int
learningRateScheduler Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. 'None'
'Step'
'WarmupCosine'
modelName Name of the model to use for training.
For more information on the available models please visit the official documentation:
/azure/machine-learning/how-to-auto-train-image-models.		 string
momentum Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. int
nesterov Enable nesterov when optimizer is 'sgd'. bool
numberOfEpochs Number of training epochs. Must be a positive integer. int
numberOfWorkers Number of data loader workers. Must be a non-negative integer. int
optimizer Type of optimizer. 'Adam'
'Adamw'
'None'
'Sgd'
randomSeed Random seed to be used when using deterministic training. int
stepLRGamma Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. int
stepLRStepSize Value of step size when learning rate scheduler is 'step'. Must be a positive integer. int
trainingBatchSize Training batch size. Must be a positive integer. int
trainingCropSize Image crop size that is input to the neural network for the training dataset. Must be a positive integer. int
validationBatchSize Validation batch size. Must be a positive integer. int
validationCropSize Image crop size that is input to the neural network for the validation dataset. Must be a positive integer. int
validationResizeSize Image size to which to resize before cropping for validation dataset. Must be a positive integer. int
warmupCosineLRCycles Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. int
warmupCosineLRWarmupEpochs Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. int
weightDecay Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. int
weightedLoss Weighted loss. The accepted values are 0 for no weighted loss.
1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.
 int

MLFlowModelJobInput

Name Description Value
description Description for the input. string
jobInputType [Required] Specifies the type of job. 'custom_model'
'literal'
'mlflow_model'
'mltable'
'triton_model'
'uri_file'
'uri_folder' (required)
mode Input Asset Delivery Mode. 'Direct'
'Download'
'EvalDownload'
'EvalMount'
'ReadOnlyMount'
'ReadWriteMount'
uri [Required] Input Asset URI. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]

ImageModelDistributionSettingsClassification

Name Description Value
amsGradient Enable AMSGrad when optimizer is 'adam' or 'adamw'. string
augmentations Settings for using Augmentations. string
beta1 Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. string
beta2 Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. string
distributed Whether to use distributer training. string
earlyStopping Enable early stopping logic during training. string
earlyStoppingDelay Minimum number of epochs or validation evaluations to wait before primary metric improvement
is tracked for early stopping. Must be a positive integer.		 string
earlyStoppingPatience Minimum number of epochs or validation evaluations with no primary metric improvement before
the run is stopped. Must be a positive integer.		 string
enableOnnxNormalization Enable normalization when exporting ONNX model. string
evaluationFrequency Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. string
gradientAccumulationStep Gradient accumulation means running a configured number of "GradAccumulationStep" steps without
updating the model weights while accumulating the gradients of those steps, and then using
the accumulated gradients to compute the weight updates. Must be a positive integer.		 string
layersToFreeze Number of layers to freeze for the model. Must be a positive integer.
For instance, passing 2 as value for 'seresnext' means
freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please
see: /azure/machine-learning/how-to-auto-train-image-models.		 string
learningRate Initial learning rate. Must be a float in the range [0, 1]. string
learningRateScheduler Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. string
modelName Name of the model to use for training.
For more information on the available models please visit the official documentation:
/azure/machine-learning/how-to-auto-train-image-models.		 string
momentum Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. string
nesterov Enable nesterov when optimizer is 'sgd'. string
numberOfEpochs Number of training epochs. Must be a positive integer. string
numberOfWorkers Number of data loader workers. Must be a non-negative integer. string
optimizer Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'. string
randomSeed Random seed to be used when using deterministic training. string
stepLRGamma Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. string
stepLRStepSize Value of step size when learning rate scheduler is 'step'. Must be a positive integer. string
trainingBatchSize Training batch size. Must be a positive integer. string
trainingCropSize Image crop size that is input to the neural network for the training dataset. Must be a positive integer. string
validationBatchSize Validation batch size. Must be a positive integer. string
validationCropSize Image crop size that is input to the neural network for the validation dataset. Must be a positive integer. string
validationResizeSize Image size to which to resize before cropping for validation dataset. Must be a positive integer. string
warmupCosineLRCycles Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. string
warmupCosineLRWarmupEpochs Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. string
weightDecay Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. string
weightedLoss Weighted loss. The accepted values are 0 for no weighted loss.
1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.
 string

ImageSweepSettings

Name Description Value
earlyTermination Type of early termination policy. EarlyTerminationPolicy
samplingAlgorithm [Required] Type of the hyperparameter sampling algorithms. 'Bayesian'
'Grid'
'Random' (required)

ImageClassificationMultilabel

Name Description Value
taskType [Required] Task type for AutoMLJob. 'ImageClassificationMultilabel' (required)
limitSettings [Required] Limit settings for the AutoML job. ImageLimitSettings (required)
modelSettings Settings used for training the model. ImageModelSettingsClassification
primaryMetric Primary metric to optimize for this task. 'AUCWeighted'
'Accuracy'
'AveragePrecisionScoreWeighted'
'IOU'
'NormMacroRecall'
'PrecisionScoreWeighted'
searchSpace Search space for sampling different combinations of models and their hyperparameters. ImageModelDistributionSettingsClassification[]
sweepSettings Model sweeping and hyperparameter sweeping related settings. ImageSweepSettings
validationData Validation data inputs. MLTableJobInput
validationDataSize The fraction of training dataset that needs to be set aside for validation purpose.
Values between (0.0 , 1.0)
Applied when validation dataset is not provided.
 int

ImageInstanceSegmentation

Name Description Value
taskType [Required] Task type for AutoMLJob. 'ImageInstanceSegmentation' (required)
limitSettings [Required] Limit settings for the AutoML job. ImageLimitSettings (required)
modelSettings Settings used for training the model. ImageModelSettingsObjectDetection
primaryMetric Primary metric to optimize for this task. 'MeanAveragePrecision'
searchSpace Search space for sampling different combinations of models and their hyperparameters. ImageModelDistributionSettingsObjectDetection[]
sweepSettings Model sweeping and hyperparameter sweeping related settings. ImageSweepSettings
validationData Validation data inputs. MLTableJobInput
validationDataSize The fraction of training dataset that needs to be set aside for validation purpose.
Values between (0.0 , 1.0)
Applied when validation dataset is not provided.
 int

ImageModelSettingsObjectDetection

Name Description Value
advancedSettings Settings for advanced scenarios. string
amsGradient Enable AMSGrad when optimizer is 'adam' or 'adamw'. bool
augmentations Settings for using Augmentations. string
beta1 Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. int
beta2 Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. int
boxDetectionsPerImage Maximum number of detections per image, for all classes. Must be a positive integer.
Note: This settings is not supported for the 'yolov5' algorithm.		 int
boxScoreThreshold During inference, only return proposals with a classification score greater than
BoxScoreThreshold. Must be a float in the range[0, 1].		 int
checkpointFrequency Frequency to store model checkpoints. Must be a positive integer. int
checkpointModel The pretrained checkpoint model for incremental training. MLFlowModelJobInput
checkpointRunId The id of a previous run that has a pretrained checkpoint for incremental training. string
distributed Whether to use distributed training. bool
earlyStopping Enable early stopping logic during training. bool
earlyStoppingDelay Minimum number of epochs or validation evaluations to wait before primary metric improvement
is tracked for early stopping. Must be a positive integer.		 int
earlyStoppingPatience Minimum number of epochs or validation evaluations with no primary metric improvement before
the run is stopped. Must be a positive integer.		 int
enableOnnxNormalization Enable normalization when exporting ONNX model. bool
evaluationFrequency Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. int
gradientAccumulationStep Gradient accumulation means running a configured number of "GradAccumulationStep" steps without
updating the model weights while accumulating the gradients of those steps, and then using
the accumulated gradients to compute the weight updates. Must be a positive integer.		 int
imageSize Image size for train and validation. Must be a positive integer.
Note: The training run may get into CUDA OOM if the size is too big.
Note: This settings is only supported for the 'yolov5' algorithm.		 int
layersToFreeze Number of layers to freeze for the model. Must be a positive integer.
For instance, passing 2 as value for 'seresnext' means
freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please
see: /azure/machine-learning/how-to-auto-train-image-models.		 int
learningRate Initial learning rate. Must be a float in the range [0, 1]. int
learningRateScheduler Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. 'None'
'Step'
'WarmupCosine'
logTrainingMetrics Enable computing and logging training metrics. 'Disable'
'Enable'
logValidationLoss Enable computing and logging validation loss. 'Disable'
'Enable'
maxSize Maximum size of the image to be rescaled before feeding it to the backbone.
Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
Note: This settings is not supported for the 'yolov5' algorithm.		 int
minSize Minimum size of the image to be rescaled before feeding it to the backbone.
Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
Note: This settings is not supported for the 'yolov5' algorithm.		 int
modelName Name of the model to use for training.
For more information on the available models please visit the official documentation:
/azure/machine-learning/how-to-auto-train-image-models.		 string
modelSize Model size. Must be 'small', 'medium', 'large', or 'xlarge'.
Note: training run may get into CUDA OOM if the model size is too big.
Note: This settings is only supported for the 'yolov5' algorithm.		 'ExtraLarge'
'Large'
'Medium'
'None'
'Small'
momentum Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. int
multiScale Enable multi-scale image by varying image size by +/- 50%.
Note: training run may get into CUDA OOM if no sufficient GPU memory.
Note: This settings is only supported for the 'yolov5' algorithm.		 bool
nesterov Enable nesterov when optimizer is 'sgd'. bool
nmsIouThreshold IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1]. int
numberOfEpochs Number of training epochs. Must be a positive integer. int
numberOfWorkers Number of data loader workers. Must be a non-negative integer. int
optimizer Type of optimizer. 'Adam'
'Adamw'
'None'
'Sgd'
randomSeed Random seed to be used when using deterministic training. int
stepLRGamma Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. int
stepLRStepSize Value of step size when learning rate scheduler is 'step'. Must be a positive integer. int
tileGridSize The grid size to use for tiling each image. Note: TileGridSize must not be
None to enable small object detection logic. A string containing two integers in mxn format.
Note: This settings is not supported for the 'yolov5' algorithm.		 string
tileOverlapRatio Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1).
Note: This settings is not supported for the 'yolov5' algorithm.		 int
tilePredictionsNmsThreshold The IOU threshold to use to perform NMS while merging predictions from tiles and image.
Used in validation/ inference. Must be float in the range [0, 1].
Note: This settings is not supported for the 'yolov5' algorithm.		 int
trainingBatchSize Training batch size. Must be a positive integer. int
validationBatchSize Validation batch size. Must be a positive integer. int
validationIouThreshold IOU threshold to use when computing validation metric. Must be float in the range [0, 1]. int
validationMetricType Metric computation method to use for validation metrics. 'Coco'
'CocoVoc'
'None'
'Voc'
warmupCosineLRCycles Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. int
warmupCosineLRWarmupEpochs Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. int
weightDecay Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. int

ImageModelDistributionSettingsObjectDetection

Name Description Value
amsGradient Enable AMSGrad when optimizer is 'adam' or 'adamw'. string
augmentations Settings for using Augmentations. string
beta1 Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. string
beta2 Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. string
boxDetectionsPerImage Maximum number of detections per image, for all classes. Must be a positive integer.
Note: This settings is not supported for the 'yolov5' algorithm.		 string
boxScoreThreshold During inference, only return proposals with a classification score greater than
BoxScoreThreshold. Must be a float in the range[0, 1].		 string
distributed Whether to use distributer training. string
earlyStopping Enable early stopping logic during training. string
earlyStoppingDelay Minimum number of epochs or validation evaluations to wait before primary metric improvement
is tracked for early stopping. Must be a positive integer.		 string
earlyStoppingPatience Minimum number of epochs or validation evaluations with no primary metric improvement before
the run is stopped. Must be a positive integer.		 string
enableOnnxNormalization Enable normalization when exporting ONNX model. string
evaluationFrequency Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. string
gradientAccumulationStep Gradient accumulation means running a configured number of "GradAccumulationStep" steps without
updating the model weights while accumulating the gradients of those steps, and then using
the accumulated gradients to compute the weight updates. Must be a positive integer.		 string
imageSize Image size for train and validation. Must be a positive integer.
Note: The training run may get into CUDA OOM if the size is too big.
Note: This settings is only supported for the 'yolov5' algorithm.		 string
layersToFreeze Number of layers to freeze for the model. Must be a positive integer.
For instance, passing 2 as value for 'seresnext' means
freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please
see: /azure/machine-learning/how-to-auto-train-image-models.		 string
learningRate Initial learning rate. Must be a float in the range [0, 1]. string
learningRateScheduler Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. string
maxSize Maximum size of the image to be rescaled before feeding it to the backbone.
Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
Note: This settings is not supported for the 'yolov5' algorithm.		 string
minSize Minimum size of the image to be rescaled before feeding it to the backbone.
Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
Note: This settings is not supported for the 'yolov5' algorithm.		 string
modelName Name of the model to use for training.
For more information on the available models please visit the official documentation:
/azure/machine-learning/how-to-auto-train-image-models.		 string
modelSize Model size. Must be 'small', 'medium', 'large', or 'xlarge'.
Note: training run may get into CUDA OOM if the model size is too big.
Note: This settings is only supported for the 'yolov5' algorithm.		 string
momentum Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. string
multiScale Enable multi-scale image by varying image size by +/- 50%.
Note: training run may get into CUDA OOM if no sufficient GPU memory.
Note: This settings is only supported for the 'yolov5' algorithm.		 string
nesterov Enable nesterov when optimizer is 'sgd'. string
nmsIouThreshold IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1]. string
numberOfEpochs Number of training epochs. Must be a positive integer. string
numberOfWorkers Number of data loader workers. Must be a non-negative integer. string
optimizer Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'. string
randomSeed Random seed to be used when using deterministic training. string
stepLRGamma Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. string
stepLRStepSize Value of step size when learning rate scheduler is 'step'. Must be a positive integer. string
tileGridSize The grid size to use for tiling each image. Note: TileGridSize must not be
None to enable small object detection logic. A string containing two integers in mxn format.
Note: This settings is not supported for the 'yolov5' algorithm.		 string
tileOverlapRatio Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1).
Note: This settings is not supported for the 'yolov5' algorithm.		 string
tilePredictionsNmsThreshold The IOU threshold to use to perform NMS while merging predictions from tiles and image.
Used in validation/ inference. Must be float in the range [0, 1].
Note: This settings is not supported for the 'yolov5' algorithm.
NMS: Non-maximum suppression		 string
trainingBatchSize Training batch size. Must be a positive integer. string
validationBatchSize Validation batch size. Must be a positive integer. string
validationIouThreshold IOU threshold to use when computing validation metric. Must be float in the range [0, 1]. string
validationMetricType Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'. string
warmupCosineLRCycles Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. string
warmupCosineLRWarmupEpochs Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. string
weightDecay Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. string

ImageObjectDetection

Name Description Value
taskType [Required] Task type for AutoMLJob. 'ImageObjectDetection' (required)
limitSettings [Required] Limit settings for the AutoML job. ImageLimitSettings (required)
modelSettings Settings used for training the model. ImageModelSettingsObjectDetection
primaryMetric Primary metric to optimize for this task. 'MeanAveragePrecision'
searchSpace Search space for sampling different combinations of models and their hyperparameters. ImageModelDistributionSettingsObjectDetection[]
sweepSettings Model sweeping and hyperparameter sweeping related settings. ImageSweepSettings
validationData Validation data inputs. MLTableJobInput
validationDataSize The fraction of training dataset that needs to be set aside for validation purpose.
Values between (0.0 , 1.0)
Applied when validation dataset is not provided.
 int

Regression

Name Description Value
taskType [Required] Task type for AutoMLJob. 'Regression' (required)
cvSplitColumnNames Columns to use for CVSplit data. string[]
featurizationSettings Featurization inputs needed for AutoML job. TableVerticalFeaturizationSettings
fixedParameters Model/training parameters that will remain constant throughout training. TableFixedParameters
limitSettings Execution constraints for AutoMLJob. TableVerticalLimitSettings
nCrossValidations Number of cross validation folds to be applied on training dataset
when validation dataset is not provided.		 NCrossValidations
primaryMetric Primary metric for regression task. 'NormalizedMeanAbsoluteError'
'NormalizedRootMeanSquaredError'
'R2Score'
'SpearmanCorrelation'
searchSpace Search space for sampling different combinations of models and their hyperparameters. TableParameterSubspace[]
sweepSettings Settings for model sweeping and hyperparameter tuning. TableSweepSettings
testData Test data input. MLTableJobInput
testDataSize The fraction of test dataset that needs to be set aside for validation purpose.
Values between (0.0 , 1.0)
Applied when validation dataset is not provided.		 int
trainingSettings Inputs for training phase for an AutoML Job. RegressionTrainingSettings
validationData Validation data inputs. MLTableJobInput
validationDataSize The fraction of training dataset that needs to be set aside for validation purpose.
Values between (0.0 , 1.0)
Applied when validation dataset is not provided.		 int
weightColumnName The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down. string

RegressionTrainingSettings

Name Description Value
allowedTrainingAlgorithms Allowed models for regression task. String array containing any of:
'DecisionTree'
'ElasticNet'
'ExtremeRandomTrees'
'GradientBoosting'
'KNN'
'LassoLars'
'LightGBM'
'RandomForest'
'SGD'
'XGBoostRegressor'
blockedTrainingAlgorithms Blocked models for regression task. String array containing any of:
'DecisionTree'
'ElasticNet'
'ExtremeRandomTrees'
'GradientBoosting'
'KNN'
'LassoLars'
'LightGBM'
'RandomForest'
'SGD'
'XGBoostRegressor'
enableDnnTraining Enable recommendation of DNN models. bool
enableModelExplainability Flag to turn on explainability on best model. bool
enableOnnxCompatibleModels Flag for enabling onnx compatible models. bool
enableStackEnsemble Enable stack ensemble run. bool
enableVoteEnsemble Enable voting ensemble run. bool
ensembleModelDownloadTimeout During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded.
Configure this parameter with a higher value than 300 secs, if more time is needed.		 string
stackEnsembleSettings Stack ensemble settings for stack ensemble run. StackEnsembleSettings
trainingMode TrainingMode mode - Setting to 'auto' is same as setting it to 'non-distributed' for now, however in the future may result in mixed mode or heuristics based mode selection. Default is 'auto'.
If 'Distributed' then only distributed featurization is used and distributed algorithms are chosen.
If 'NonDistributed' then only non distributed algorithms are chosen.
 'Auto'
'Distributed'
'NonDistributed'

TextClassification

Name Description Value
taskType [Required] Task type for AutoMLJob. 'TextClassification' (required)
featurizationSettings Featurization inputs needed for AutoML job. NlpVerticalFeaturizationSettings
fixedParameters Model/training parameters that will remain constant throughout training. NlpFixedParameters
limitSettings Execution constraints for AutoMLJob. NlpVerticalLimitSettings
primaryMetric Primary metric for Text-Classification task. 'AUCWeighted'
'Accuracy'
'AveragePrecisionScoreWeighted'
'NormMacroRecall'
'PrecisionScoreWeighted'
searchSpace Search space for sampling different combinations of models and their hyperparameters. NlpParameterSubspace[]
sweepSettings Settings for model sweeping and hyperparameter tuning. NlpSweepSettings
validationData Validation data inputs. MLTableJobInput

NlpVerticalFeaturizationSettings

Name Description Value
datasetLanguage Dataset language, useful for the text data. string

NlpFixedParameters

Name Description Value
gradientAccumulationSteps Number of steps to accumulate gradients over before running a backward pass. int
learningRate The learning rate for the training procedure. int
learningRateScheduler The type of learning rate schedule to use during the training procedure. 'Constant'
'ConstantWithWarmup'
'Cosine'
'CosineWithRestarts'
'Linear'
'None'
'Polynomial'
modelName The name of the model to train. string
numberOfEpochs Number of training epochs. int
trainingBatchSize The batch size for the training procedure. int
validationBatchSize The batch size to be used during evaluation. int
warmupRatio The warmup ratio, used alongside LrSchedulerType. int
weightDecay The weight decay for the training procedure. int

NlpVerticalLimitSettings

Name Description Value
maxConcurrentTrials Maximum Concurrent AutoML iterations. int
maxNodes Maximum nodes to use for the experiment. int
maxTrials Number of AutoML iterations. int
timeout AutoML job timeout. string
trialTimeout Timeout for individual HD trials. string

NlpParameterSubspace

Name Description Value
gradientAccumulationSteps Number of steps to accumulate gradients over before running a backward pass. string
learningRate The learning rate for the training procedure. string
learningRateScheduler The type of learning rate schedule to use during the training procedure. string
modelName The name of the model to train. string
numberOfEpochs Number of training epochs. string
trainingBatchSize The batch size for the training procedure. string
validationBatchSize The batch size to be used during evaluation. string
warmupRatio The warmup ratio, used alongside LrSchedulerType. string
weightDecay The weight decay for the training procedure. string

NlpSweepSettings

Name Description Value
earlyTermination Type of early termination policy for the sweeping job. EarlyTerminationPolicy
samplingAlgorithm [Required] Type of sampling algorithm. 'Bayesian'
'Grid'
'Random' (required)

TextClassificationMultilabel

Name Description Value
taskType [Required] Task type for AutoMLJob. 'TextClassificationMultilabel' (required)
featurizationSettings Featurization inputs needed for AutoML job. NlpVerticalFeaturizationSettings
fixedParameters Model/training parameters that will remain constant throughout training. NlpFixedParameters
limitSettings Execution constraints for AutoMLJob. NlpVerticalLimitSettings
searchSpace Search space for sampling different combinations of models and their hyperparameters. NlpParameterSubspace[]
sweepSettings Settings for model sweeping and hyperparameter tuning. NlpSweepSettings
validationData Validation data inputs. MLTableJobInput

TextNer

Name Description Value
taskType [Required] Task type for AutoMLJob. 'TextNER' (required)
featurizationSettings Featurization inputs needed for AutoML job. NlpVerticalFeaturizationSettings
fixedParameters Model/training parameters that will remain constant throughout training. NlpFixedParameters
limitSettings Execution constraints for AutoMLJob. NlpVerticalLimitSettings
searchSpace Search space for sampling different combinations of models and their hyperparameters. NlpParameterSubspace[]
sweepSettings Settings for model sweeping and hyperparameter tuning. NlpSweepSettings
validationData Validation data inputs. MLTableJobInput

CommandJob

Name Description Value
jobType [Required] Specifies the type of job. 'Command' (required)
autologgerSettings Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null. AutologgerSettings
codeId ARM resource ID of the code asset. string
command [Required] The command to execute on startup of the job. eg. "python train.py" string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]
distribution Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, Ray, or null. DistributionConfiguration
environmentId [Required] The ARM resource ID of the Environment specification for the job. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]
environmentVariables Environment variables included in the job. CommandJobEnvironmentVariables
inputs Mapping of input data bindings used in the job. CommandJobInputs
limits Command Job limit. CommandJobLimits
outputs Mapping of output data bindings used in the job. CommandJobOutputs
queueSettings Queue settings for the job QueueSettings
resources Compute Resource configuration for the job. JobResourceConfiguration

AutologgerSettings

Name Description Value
mlflowAutologger [Required] Indicates whether mlflow autologger is enabled. 'Disabled'
'Enabled' (required)

DistributionConfiguration

Name Description Value
distributionType Set the object type Mpi
PyTorch
Ray
TensorFlow (required)

Mpi

Name Description Value
distributionType [Required] Specifies the type of distribution framework. 'Mpi' (required)
processCountPerInstance Number of processes per MPI node. int

PyTorch

Name Description Value
distributionType [Required] Specifies the type of distribution framework. 'PyTorch' (required)
processCountPerInstance Number of processes per node. int

Ray

Name Description Value
distributionType [Required] Specifies the type of distribution framework. 'Ray' (required)
address The address of Ray head node. string
dashboardPort The port to bind the dashboard server to. int
headNodeAdditionalArgs Additional arguments passed to ray start in head node. string
includeDashboard Provide this argument to start the Ray dashboard GUI. bool
port The port of the head ray process. int
workerNodeAdditionalArgs Additional arguments passed to ray start in worker node. string

TensorFlow

Name Description Value
distributionType [Required] Specifies the type of distribution framework. 'TensorFlow' (required)
parameterServerCount Number of parameter server tasks. int
workerCount Number of workers. If not specified, will default to the instance count. int

CommandJobEnvironmentVariables

Name Description Value
{customized property} string

CommandJobInputs

Name Description Value
{customized property} JobInput

JobInput

Name Description Value
description Description for the input. string
jobInputType Set the object type custom_model
literal
mlflow_model
mltable
triton_model
uri_file
uri_folder (required)

CustomModelJobInput

Name Description Value
jobInputType [Required] Specifies the type of job. 'custom_model' (required)
mode Input Asset Delivery Mode. 'Direct'
'Download'
'EvalDownload'
'EvalMount'
'ReadOnlyMount'
'ReadWriteMount'
uri [Required] Input Asset URI. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]

LiteralJobInput

Name Description Value
jobInputType [Required] Specifies the type of job. 'literal' (required)
value [Required] Literal value for the input. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]

TritonModelJobInput

Name Description Value
jobInputType [Required] Specifies the type of job. 'triton_model' (required)
mode Input Asset Delivery Mode. 'Direct'
'Download'
'EvalDownload'
'EvalMount'
'ReadOnlyMount'
'ReadWriteMount'
uri [Required] Input Asset URI. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]

UriFileJobInput

Name Description Value
jobInputType [Required] Specifies the type of job. 'uri_file' (required)
mode Input Asset Delivery Mode. 'Direct'
'Download'
'EvalDownload'
'EvalMount'
'ReadOnlyMount'
'ReadWriteMount'
uri [Required] Input Asset URI. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]

UriFolderJobInput

Name Description Value
jobInputType [Required] Specifies the type of job. 'uri_folder' (required)
mode Input Asset Delivery Mode. 'Direct'
'Download'
'EvalDownload'
'EvalMount'
'ReadOnlyMount'
'ReadWriteMount'
uri [Required] Input Asset URI. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]

CommandJobLimits

Name Description Value
jobLimitsType [Required] JobLimit type. 'Command'
'Sweep' (required)
timeout The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds. string

CommandJobOutputs

Name Description Value
{customized property} JobOutput

LabelingJobProperties

Name Description Value
componentId ARM resource ID of the component resource. string
computeId ARM resource ID of the compute resource. string
dataConfiguration Configuration of data used in the job. LabelingDataConfiguration
description The asset description text. string
displayName Display name of job. string
experimentName The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment. string
identity Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null.
Defaults to AmlToken if null.		 IdentityConfiguration
isArchived Is the asset archived? bool
jobInstructions Labeling instructions of the job. LabelingJobInstructions
jobType [Required] Specifies the type of job. 'AutoML'
'Command'
'Labeling'
'Pipeline'
'Spark'
'Sweep' (required)
labelCategories Label categories of the job. LabelingJobLabelCategories
labelingJobMediaProperties Media type specific properties in the job. LabelingJobMediaProperties
mlAssistConfiguration Configuration of MLAssist feature in the job. MLAssistConfiguration
notificationSetting Notification setting for the job NotificationSetting
properties The asset property dictionary. ResourceBaseProperties
secretsConfiguration Configuration for secrets to be made available during runtime. JobBaseSecretsConfiguration
services List of JobEndpoints.
For local jobs, a job endpoint will have an endpoint value of FileStreamObject.		 JobBaseServices
tags Tag dictionary. Tags can be added, removed, and updated. object

LabelingDataConfiguration

Name Description Value
dataId Resource Id of the data asset to perform labeling. string
incrementalDataRefresh Indicates whether to enable incremental data refresh. 'Disabled'
'Enabled'

LabelingJobInstructions

Name Description Value
uri The link to a page with detailed labeling instructions for labelers. string

LabelingJobLabelCategories

Name Description Value
{customized property} LabelCategory

LabelCategory

Name Description Value
classes Dictionary of label classes in this category. LabelCategoryClasses
displayName Display name of the label category. string
multiSelect Indicates whether it is allowed to select multiple classes in this category. 'Disabled'
'Enabled'

LabelCategoryClasses

Name Description Value
{customized property} LabelClass

LabelClass

Name Description Value
displayName Display name of the label class. string
subclasses Dictionary of subclasses of the label class. LabelClassSubclasses

LabelClassSubclasses

Name Description Value
{customized property} LabelClass

LabelingJobMediaProperties

Name Description Value
mediaType Set the object type Image
Text (required)

LabelingJobImageProperties

Name Description Value
mediaType [Required] Media type of the job. 'Image' (required)
annotationType Annotation type of image labeling job. 'BoundingBox'
'Classification'
'InstanceSegmentation'

LabelingJobTextProperties

Name Description Value
mediaType [Required] Media type of the job. 'Text' (required)
annotationType Annotation type of text labeling job. 'Classification'
'NamedEntityRecognition'

MLAssistConfiguration

Name Description Value
mlAssist Set the object type Disabled
Enabled (required)

MLAssistConfigurationDisabled

Name Description Value
mlAssist [Required] Indicates whether MLAssist feature is enabled. 'Disabled' (required)

MLAssistConfigurationEnabled

Name Description Value
mlAssist [Required] Indicates whether MLAssist feature is enabled. 'Enabled' (required)
inferencingComputeBinding [Required] AML compute binding used in inferencing. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]
trainingComputeBinding [Required] AML compute binding used in training. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]

PipelineJob

Name Description Value
jobType [Required] Specifies the type of job. 'Pipeline' (required)
inputs Inputs for the pipeline job. PipelineJobInputs
jobs Jobs construct the Pipeline Job. PipelineJobJobs
outputs Outputs for the pipeline job PipelineJobOutputs
settings Pipeline settings, for things like ContinueRunOnStepFailure etc. For Bicep, you can use the any() function.
sourceJobId ARM resource ID of source job. string

PipelineJobInputs

Name Description Value
{customized property} JobInput

PipelineJobJobs

Name Description Value
{customized property} For Bicep, you can use the any() function.

PipelineJobOutputs

Name Description Value
{customized property} JobOutput

SparkJob

Name Description Value
jobType [Required] Specifies the type of job. 'Spark' (required)
archives Archive files used in the job. string[]
args Arguments for the job. string
codeId [Required] ARM resource ID of the code asset. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]
conf Spark configured properties. SparkJobConf
entry [Required] The entry to execute on startup of the job. SparkJobEntry (required)
environmentId The ARM resource ID of the Environment specification for the job. string
files Files used in the job. string[]
inputs Mapping of input data bindings used in the job. SparkJobInputs
jars Jar files used in the job. string[]
outputs Mapping of output data bindings used in the job. SparkJobOutputs
pyFiles Python files used in the job. string[]
queueSettings Queue settings for the job QueueSettings
resources Compute Resource configuration for the job. SparkResourceConfiguration

SparkJobConf

Name Description Value
{customized property} string

SparkJobEntry

Name Description Value
sparkJobEntryType Set the object type SparkJobPythonEntry
SparkJobScalaEntry (required)

SparkJobPythonEntry

Name Description Value
sparkJobEntryType [Required] Type of the job's entry point. 'SparkJobPythonEntry' (required)
file [Required] Relative python file path for job entry point. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]

SparkJobScalaEntry

Name Description Value
sparkJobEntryType [Required] Type of the job's entry point. 'SparkJobScalaEntry' (required)
className [Required] Scala class name used as entry point. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]

SparkJobInputs

Name Description Value
{customized property} JobInput

SparkJobOutputs

Name Description Value
{customized property} JobOutput

SparkResourceConfiguration

Name Description Value
instanceType Optional type of VM used as supported by the compute target. string
runtimeVersion Version of spark runtime used for the job. string

SweepJob

Name Description Value
jobType [Required] Specifies the type of job. 'Sweep' (required)
earlyTermination Early termination policies enable canceling poor-performing runs before they complete EarlyTerminationPolicy
inputs Mapping of input data bindings used in the job. SweepJobInputs
limits Sweep Job limit. SweepJobLimits
objective [Required] Optimization objective. Objective (required)
outputs Mapping of output data bindings used in the job. SweepJobOutputs
queueSettings Queue settings for the job QueueSettings
samplingAlgorithm [Required] The hyperparameter sampling algorithm SamplingAlgorithm (required)
searchSpace [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter For Bicep, you can use the any() function.(required)
trial [Required] Trial component definition. TrialComponent (required)

SweepJobInputs

Name Description Value
{customized property} JobInput

SweepJobLimits

Name Description Value
jobLimitsType [Required] JobLimit type. 'Command'
'Sweep' (required)
maxConcurrentTrials Sweep Job max concurrent trials. int
maxTotalTrials Sweep Job max total trials. int
timeout The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds. string
trialTimeout Sweep Job Trial timeout value. string

Objective

Name Description Value
goal [Required] Defines supported metric goals for hyperparameter tuning 'Maximize'
'Minimize' (required)
primaryMetric [Required] Name of the metric to optimize. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]

SweepJobOutputs

Name Description Value
{customized property} JobOutput

SamplingAlgorithm

Name Description Value
samplingAlgorithmType Set the object type Bayesian
Grid
Random (required)

BayesianSamplingAlgorithm

Name Description Value
samplingAlgorithmType [Required] The algorithm used for generating hyperparameter values, along with configuration properties 'Bayesian' (required)

GridSamplingAlgorithm

Name Description Value
samplingAlgorithmType [Required] The algorithm used for generating hyperparameter values, along with configuration properties 'Grid' (required)

RandomSamplingAlgorithm

Name Description Value
samplingAlgorithmType [Required] The algorithm used for generating hyperparameter values, along with configuration properties 'Random' (required)
logbase An optional positive number or e in string format to be used as base for log based random sampling string
rule The specific type of random algorithm 'Random'
'Sobol'
seed An optional integer to use as the seed for random number generation int

TrialComponent

Name Description Value
codeId ARM resource ID of the code asset. string
command [Required] The command to execute on startup of the job. eg. "python train.py" string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]
distribution Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null. DistributionConfiguration
environmentId [Required] The ARM resource ID of the Environment specification for the job. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]
environmentVariables Environment variables included in the job. TrialComponentEnvironmentVariables
resources Compute Resource configuration for the job. JobResourceConfiguration

TrialComponentEnvironmentVariables

Name Description Value
{customized property} string

CreateMonitorAction

Name Description Value
actionType [Required] Specifies the action type of the schedule 'CreateMonitor' (required)
monitorDefinition [Required] Defines the monitor. MonitorDefinition (required)

MonitorDefinition

Name Description Value
alertNotificationSetting The monitor's notification settings. MonitoringAlertNotificationSettingsBase
computeId [Required] The ARM resource ID of the compute resource to run the monitoring job on. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]
monitoringTarget The ARM resource ID of either the model or deployment targeted by this monitor. string
signals [Required] The signals to monitor. MonitorDefinitionSignals (required)

MonitoringAlertNotificationSettingsBase

Name Description Value
alertNotificationType Set the object type AzureMonitor
Email (required)

AzMonMonitoringAlertNotificationSettings

Name Description Value
alertNotificationType [Required] Specifies the type of signal to monitor. 'AzureMonitor' (required)

EmailMonitoringAlertNotificationSettings

Name Description Value
alertNotificationType [Required] Specifies the type of signal to monitor. 'Email' (required)
emailNotificationSetting Configuration for notification. NotificationSetting

MonitorDefinitionSignals

Name Description Value
{customized property} MonitoringSignalBase

MonitoringSignalBase

Name Description Value
lookbackPeriod The amount of time a single monitor should look back over the target data on a given run. string
mode The current notification mode for this signal. 'Disabled'
'Enabled'
signalType Set the object type Custom
DataDrift
DataQuality
FeatureAttributionDrift
ModelPerformance
PredictionDrift (required)

CustomMonitoringSignal

Name Description Value
signalType [Required] Specifies the type of signal to monitor. 'Custom' (required)
componentId [Required] ARM resource ID of the component resource used to calculate the custom metrics. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]
inputAssets Monitoring assets to take as input. Key is the component input port name, value is the data asset. CustomMonitoringSignalInputAssets
metricThresholds [Required] A list of metrics to calculate and their associated thresholds. CustomMetricThreshold[] (required)

CustomMonitoringSignalInputAssets

Name Description Value
{customized property} MonitoringInputData

MonitoringInputData

Name Description Value
asset The data asset input to be leveraged by the monitoring job.. For Bicep, you can use the any() function.
dataContext [Required] The context of the data source. 'GroundTruth'
'ModelInputs'
'ModelOutputs'
'Test'
'Training'
'Validation' (required)
preprocessingComponentId The ARM resource ID of the component resource used to preprocess the data. string
targetColumnName The target column in the given data asset to leverage. string

CustomMetricThreshold

Name Description Value
metric [Required] The user-defined metric to calculate. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]
threshold The threshold value. If null, a default value will be set depending on the selected metric. MonitoringThreshold

MonitoringThreshold

Name Description Value
value The threshold value. If null, the set default is dependent on the metric type. int

DataDriftMonitoringSignal

Name Description Value
signalType [Required] Specifies the type of signal to monitor. 'DataDrift' (required)
baselineData [Required] The data to calculate drift against. MonitoringInputData (required)
dataSegment The data segment used for scoping on a subset of the data population. MonitoringDataSegment
features The feature filter which identifies which feature to calculate drift over. MonitoringFeatureFilterBase
metricThresholds [Required] A list of metrics to calculate and their associated thresholds. DataDriftMetricThresholdBase[] (required)
targetData [Required] The data which drift will be calculated for. MonitoringInputData (required)

MonitoringDataSegment

Name Description Value
feature The feature to segment the data on. string
values Filters for only the specified values of the given segmented feature. string[]

MonitoringFeatureFilterBase

Name Description Value
filterType Set the object type AllFeatures
FeatureSubset
TopNByAttribution (required)

AllFeatures

Name Description Value
filterType [Required] Specifies the feature filter to leverage when selecting features to calculate metrics over. 'AllFeatures' (required)

FeatureSubset

Name Description Value
filterType [Required] Specifies the feature filter to leverage when selecting features to calculate metrics over. 'FeatureSubset' (required)
features [Required] The list of features to include. string[] (required)

TopNFeaturesByAttribution

Name Description Value
filterType [Required] Specifies the feature filter to leverage when selecting features to calculate metrics over. 'TopNByAttribution' (required)
top The number of top features to include. int

DataDriftMetricThresholdBase

Name Description Value
threshold The threshold value. If null, a default value will be set depending on the selected metric. MonitoringThreshold
dataType Set the object type Categorical
Numerical (required)

CategoricalDataDriftMetricThreshold

Name Description Value
dataType [Required] Specifies the data type of the metric threshold. 'Categorical' (required)
metric [Required] The categorical data drift metric to calculate. 'JensenShannonDistance'
'PearsonsChiSquaredTest'
'PopulationStabilityIndex' (required)

NumericalDataDriftMetricThreshold

Name Description Value
dataType [Required] Specifies the data type of the metric threshold. 'Numerical' (required)
metric [Required] The numerical data drift metric to calculate. 'JensenShannonDistance'
'NormalizedWassersteinDistance'
'PopulationStabilityIndex'
'TwoSampleKolmogorovSmirnovTest' (required)

DataQualityMonitoringSignal

Name Description Value
signalType [Required] Specifies the type of signal to monitor. 'DataQuality' (required)
baselineData [Required] The data to calculate drift against. MonitoringInputData (required)
features The features to calculate drift over. MonitoringFeatureFilterBase
metricThresholds [Required] A list of metrics to calculate and their associated thresholds. DataQualityMetricThresholdBase[] (required)
targetData [Required] The data produced by the production service which drift will be calculated for. MonitoringInputData (required)

DataQualityMetricThresholdBase

Name Description Value
threshold The threshold value. If null, a default value will be set depending on the selected metric. MonitoringThreshold
dataType Set the object type Categorical
Numerical (required)

CategoricalDataQualityMetricThreshold

Name Description Value
dataType [Required] Specifies the data type of the metric threshold. 'Categorical' (required)
metric [Required] The categorical data quality metric to calculate. 'DataTypeErrorRate'
'NullValueRate'
'OutOfBoundsRate' (required)

NumericalDataQualityMetricThreshold

Name Description Value
dataType [Required] Specifies the data type of the metric threshold. 'Numerical' (required)
metric [Required] The numerical data quality metric to calculate. 'DataTypeErrorRate'
'NullValueRate'
'OutOfBoundsRate' (required)

FeatureAttributionDriftMonitoringSignal

Name Description Value
signalType [Required] Specifies the type of signal to monitor. 'FeatureAttributionDrift' (required)
baselineData [Required] The data to calculate drift against. MonitoringInputData (required)
metricThreshold [Required] A list of metrics to calculate and their associated thresholds. FeatureAttributionMetricThreshold (required)
modelType [Required] The type of task the model performs. 'Classification'
'Regression' (required)
targetData [Required] The data which drift will be calculated for. MonitoringInputData (required)

FeatureAttributionMetricThreshold

Name Description Value
metric [Required] The feature attribution metric to calculate. 'NormalizedDiscountedCumulativeGain' (required)
threshold The threshold value. If null, a default value will be set depending on the selected metric. MonitoringThreshold

ModelPerformanceSignalBase

Name Description Value
signalType [Required] Specifies the type of signal to monitor. 'ModelPerformance' (required)
baselineData [Required] The data to calculate drift against. MonitoringInputData (required)
dataSegment The data segment. MonitoringDataSegment
metricThreshold [Required] A list of metrics to calculate and their associated thresholds. ModelPerformanceMetricThresholdBase (required)
targetData [Required] The data produced by the production service which drift will be calculated for. MonitoringInputData (required)

ModelPerformanceMetricThresholdBase

Name Description Value
threshold The threshold value. If null, a default value will be set depending on the selected metric. MonitoringThreshold
modelType Set the object type Classification
Regression (required)

ClassificationModelPerformanceMetricThreshold

Name Description Value
modelType [Required] Specifies the data type of the metric threshold. 'Classification' (required)
metric [Required] The classification model performance to calculate. 'Accuracy'
'F1Score'
'Precision'
'Recall' (required)

RegressionModelPerformanceMetricThreshold

Name Description Value
modelType [Required] Specifies the data type of the metric threshold. 'Regression' (required)
metric [Required] The regression model performance metric to calculate. 'MeanAbsoluteError'
'MeanSquaredError'
'RootMeanSquaredError' (required)

PredictionDriftMonitoringSignal

Name Description Value
signalType [Required] Specifies the type of signal to monitor. 'PredictionDrift' (required)
baselineData [Required] The data to calculate drift against. MonitoringInputData (required)
metricThresholds [Required] A list of metrics to calculate and their associated thresholds. PredictionDriftMetricThresholdBase[] (required)
modelType [Required] The type of the model monitored. 'Classification'
'Regression' (required)
targetData [Required] The data which drift will be calculated for. MonitoringInputData (required)

PredictionDriftMetricThresholdBase

Name Description Value
threshold The threshold value. If null, a default value will be set depending on the selected metric. MonitoringThreshold
dataType Set the object type Categorical
Numerical (required)

CategoricalPredictionDriftMetricThreshold

Name Description Value
dataType [Required] Specifies the data type of the metric threshold. 'Categorical' (required)
metric [Required] The categorical prediction drift metric to calculate. 'JensenShannonDistance'
'PearsonsChiSquaredTest'
'PopulationStabilityIndex' (required)

NumericalPredictionDriftMetricThreshold

Name Description Value
dataType [Required] Specifies the data type of the metric threshold. 'Numerical' (required)
metric [Required] The numerical prediction drift metric to calculate. 'JensenShannonDistance'
'NormalizedWassersteinDistance'
'PopulationStabilityIndex'
'TwoSampleKolmogorovSmirnovTest' (required)

ImportDataAction

Name Description Value
actionType [Required] Specifies the action type of the schedule 'ImportData' (required)
dataImportDefinition [Required] Defines Schedule action definition details. DataImport (required)

DataImport

Name Description Value
assetName Name of the asset for data import job to create string
autoDeleteSetting Specifies the lifecycle setting of managed data asset. AutoDeleteSetting
dataType [Required] Specifies the type of data. 'mltable'
'uri_file'
'uri_folder' (required)
dataUri [Required] Uri of the data. Example: https://go.microsoft.com/fwlink/?linkid=2202330 string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]
description The asset description text. string
intellectualProperty Intellectual Property details. Used if data is an Intellectual Property. IntellectualProperty
isAnonymous If the name version are system generated (anonymous registration). For types where Stage is defined, when Stage is provided it will be used to populate IsAnonymous bool
isArchived Is the asset archived? For types where Stage is defined, when Stage is provided it will be used to populate IsArchived bool
properties The asset property dictionary. ResourceBaseProperties
source Source data of the asset to import from DataImportSource
stage Stage in the data lifecycle assigned to this data asset string
tags Tag dictionary. Tags can be added, removed, and updated. object

IntellectualProperty

Name Description Value
protectionLevel Protection level of the Intellectual Property. 'All'
'None'
publisher [Required] Publisher of the Intellectual Property. Must be the same as Registry publisher name. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]

DataImportSource

Name Description Value
connection Workspace connection for data import source storage string
sourceType Set the object type database
file_system (required)

DatabaseSource

Name Description Value
sourceType [Required] Specifies the type of data. 'database' (required)
query SQL Query statement for data import Database source string
storedProcedure SQL StoredProcedure on data import Database source string
storedProcedureParams SQL StoredProcedure parameters DatabaseSourceStoredProcedureParamsItem[]
tableName Name of the table on data import Database source string

DatabaseSourceStoredProcedureParamsItem

Name Description Value
{customized property} string

FileSystemSource

Name Description Value
sourceType [Required] Specifies the type of data. 'file_system' (required)
path Path on data import FileSystem source string

EndpointScheduleAction

Name Description Value
actionType [Required] Specifies the action type of the schedule 'InvokeBatchEndpoint' (required)
endpointInvocationDefinition [Required] Defines Schedule action definition details.
{see href="TBD" /}

 For Bicep, you can use the any() function.(required)

TriggerBase

Name Description Value
endTime Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer https://en.wikipedia.org/wiki/ISO_8601.
Recommented format would be "2022-06-01T00:00:01"
If not present, the schedule will run indefinitely		 string
startTime Specifies start time of schedule in ISO 8601 format, but without a UTC offset. string
timeZone Specifies time zone in which the schedule runs.
TimeZone should follow Windows time zone format. Refer: /windows-hardware/manufacture/desktop/default-time-zones />		 string
triggerType Set the object type Cron
Recurrence (required)

CronTrigger

Name Description Value
triggerType [Required] 'Cron' (required)
expression [Required] Specifies cron expression of schedule.
The expression should follow NCronTab format.		 string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]

RecurrenceTrigger

Name Description Value
endTime Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer https://en.wikipedia.org/wiki/ISO_8601.
Recommented format would be "2022-06-01T00:00:01"
If not present, the schedule will run indefinitely		 string
frequency [Required] The frequency to trigger schedule. 'Day'
'Hour'
'Minute'
'Month'
'Week' (required)
interval [Required] Specifies schedule interval in conjunction with frequency int (required)
schedule The recurrence schedule. RecurrenceSchedule
startTime Specifies start time of schedule in ISO 8601 format, but without a UTC offset. string
timeZone Specifies time zone in which the schedule runs.
TimeZone should follow Windows time zone format. Refer: /windows-hardware/manufacture/desktop/default-time-zones		 string
triggerType [Required] 'Cron'
'Recurrence' (required)

RecurrenceSchedule

Name Description Value
hours [Required] List of hours for the schedule. int[] (required)
minutes [Required] List of minutes for the schedule. int[] (required)
monthDays List of month days for the schedule int[]
weekDays List of days for the schedule. String array containing any of:
'Friday'
'Monday'
'Saturday'
'Sunday'
'Thursday'
'Tuesday'
'Wednesday'

ARM template resource definition

The workspaces/schedules resource type can be deployed with operations that target:

For a list of changed properties in each API version, see change log.

Resource format

To create a Microsoft.MachineLearningServices/workspaces/schedules resource, add the following JSON to your template.

{
  "type": "Microsoft.MachineLearningServices/workspaces/schedules",
  "apiVersion": "2023-04-01-preview",
  "name": "string",
  "properties": {
    "action": {
      "actionType": "string"
      // For remaining properties, see ScheduleActionBase objects
    },
    "description": "string",
    "displayName": "string",
    "isEnabled": "bool",
    "properties": {
      "{customized property}": "string"
    },
    "tags": {},
    "trigger": {
      "endTime": "string",
      "startTime": "string",
      "timeZone": "string",
      "triggerType": "string"
      // For remaining properties, see TriggerBase objects
    }
  }
}

ScheduleActionBase objects

Set the actionType property to specify the type of object.

For CreateJob, use:

  "actionType": "CreateJob",
  "jobDefinition": {
    "componentId": "string",
    "computeId": "string",
    "description": "string",
    "displayName": "string",
    "experimentName": "string",
    "identity": {
      "identityType": "string"
      // For remaining properties, see IdentityConfiguration objects
    },
    "isArchived": "bool",
    "notificationSetting": {
      "emailOn": [ "string" ],
      "emails": [ "string" ],
      "webhooks": {
        "{customized property}": {
          "eventType": "string",
          "webhookType": "string"
          // For remaining properties, see Webhook objects
        }
      }
    },
    "properties": {
      "{customized property}": "string"
    },
    "secretsConfiguration": {
      "{customized property}": {
        "uri": "string",
        "workspaceSecretName": "string"
      }
    },
    "services": {
      "{customized property}": {
        "endpoint": "string",
        "jobServiceType": "string",
        "nodes": {
          "nodesValueType": "string"
          // For remaining properties, see Nodes objects
        },
        "port": "int",
        "properties": {
          "{customized property}": "string"
        }
      }
    },
    "tags": {},
    "jobType": "string"
    // For remaining properties, see JobBaseProperties objects
  }

For CreateMonitor, use:

  "actionType": "CreateMonitor",
  "monitorDefinition": {
    "alertNotificationSetting": {
      "alertNotificationType": "string"
      // For remaining properties, see MonitoringAlertNotificationSettingsBase objects
    },
    "computeId": "string",
    "monitoringTarget": "string",
    "signals": {
      "{customized property}": {
        "lookbackPeriod": "string",
        "mode": "string",
        "signalType": "string"
        // For remaining properties, see MonitoringSignalBase objects
      }
    }
  }

For ImportData, use:

  "actionType": "ImportData",
  "dataImportDefinition": {
    "assetName": "string",
    "autoDeleteSetting": {
      "condition": "string",
      "value": "string"
    },
    "dataType": "string",
    "dataUri": "string",
    "description": "string",
    "intellectualProperty": {
      "protectionLevel": "string",
      "publisher": "string"
    },
    "isAnonymous": "bool",
    "isArchived": "bool",
    "properties": {
      "{customized property}": "string"
    },
    "source": {
      "connection": "string",
      "sourceType": "string"
      // For remaining properties, see DataImportSource objects
    },
    "stage": "string",
    "tags": {}
  }

For InvokeBatchEndpoint, use:

  "actionType": "InvokeBatchEndpoint",
  "endpointInvocationDefinition": {}

JobBaseProperties objects

Set the jobType property to specify the type of object.

For AutoML, use:

  "jobType": "AutoML",
  "environmentId": "string",
  "environmentVariables": {
    "{customized property}": "string"
  },
  "outputs": {
    "{customized property}": {
      "description": "string",
      "jobOutputType": "string"
      // For remaining properties, see JobOutput objects
    }
  },
  "queueSettings": {
    "jobTier": "string",
    "priority": "int"
  },
  "resources": {
    "dockerArgs": "string",
    "instanceCount": "int",
    "instanceType": "string",
    "locations": [ "string" ],
    "maxInstanceCount": "int",
    "properties": {
      "{customized property}": {}
    },
    "shmSize": "string"
  },
  "taskDetails": {
    "logVerbosity": "string",
    "targetColumnName": "string",
    "trainingData": {
      "description": "string",
      "jobInputType": "string",
      "mode": "string",
      "uri": "string"
    },
    "taskType": "string"
    // For remaining properties, see AutoMLVertical objects
  }

For Command, use:

  "jobType": "Command",
  "autologgerSettings": {
    "mlflowAutologger": "string"
  },
  "codeId": "string",
  "command": "string",
  "distribution": {
    "distributionType": "string"
    // For remaining properties, see DistributionConfiguration objects
  },
  "environmentId": "string",
  "environmentVariables": {
    "{customized property}": "string"
  },
  "inputs": {
    "{customized property}": {
      "description": "string",
      "jobInputType": "string"
      // For remaining properties, see JobInput objects
    }
  },
  "limits": {
    "jobLimitsType": "string",
    "timeout": "string"
  },
  "outputs": {
    "{customized property}": {
      "description": "string",
      "jobOutputType": "string"
      // For remaining properties, see JobOutput objects
    }
  },
  "queueSettings": {
    "jobTier": "string",
    "priority": "int"
  },
  "resources": {
    "dockerArgs": "string",
    "instanceCount": "int",
    "instanceType": "string",
    "locations": [ "string" ],
    "maxInstanceCount": "int",
    "properties": {
      "{customized property}": {}
    },
    "shmSize": "string"
  }

For Labeling, use:

  "jobType": "Labeling",
  "dataConfiguration": {
    "dataId": "string",
    "incrementalDataRefresh": "string"
  },
  "jobInstructions": {
    "uri": "string"
  },
  "labelCategories": {
    "{customized property}": {
      "classes": {
        "{customized property}": {
          "displayName": "string",
          "subclasses": {
            "{customized property}": {}
        }
      },
      "displayName": "string",
      "multiSelect": "string"
    }
  },
  "labelingJobMediaProperties": {
    "mediaType": "string"
    // For remaining properties, see LabelingJobMediaProperties objects
  },
  "mlAssistConfiguration": {
    "mlAssist": "string"
    // For remaining properties, see MLAssistConfiguration objects
  }

For Pipeline, use:

  "jobType": "Pipeline",
  "inputs": {
    "{customized property}": {
      "description": "string",
      "jobInputType": "string"
      // For remaining properties, see JobInput objects
    }
  },
  "jobs": {
    "{customized property}": {}
  },
  "outputs": {
    "{customized property}": {
      "description": "string",
      "jobOutputType": "string"
      // For remaining properties, see JobOutput objects
    }
  },
  "settings": {},
  "sourceJobId": "string"

For Spark, use:

  "jobType": "Spark",
  "archives": [ "string" ],
  "args": "string",
  "codeId": "string",
  "conf": {
    "{customized property}": "string"
  },
  "entry": {
    "sparkJobEntryType": "string"
    // For remaining properties, see SparkJobEntry objects
  },
  "environmentId": "string",
  "files": [ "string" ],
  "inputs": {
    "{customized property}": {
      "description": "string",
      "jobInputType": "string"
      // For remaining properties, see JobInput objects
    }
  },
  "jars": [ "string" ],
  "outputs": {
    "{customized property}": {
      "description": "string",
      "jobOutputType": "string"
      // For remaining properties, see JobOutput objects
    }
  },
  "pyFiles": [ "string" ],
  "queueSettings": {
    "jobTier": "string",
    "priority": "int"
  },
  "resources": {
    "instanceType": "string",
    "runtimeVersion": "string"
  }

For Sweep, use:

  "jobType": "Sweep",
  "earlyTermination": {
    "delayEvaluation": "int",
    "evaluationInterval": "int",
    "policyType": "string"
    // For remaining properties, see EarlyTerminationPolicy objects
  },
  "inputs": {
    "{customized property}": {
      "description": "string",
      "jobInputType": "string"
      // For remaining properties, see JobInput objects
    }
  },
  "limits": {
    "jobLimitsType": "string",
    "maxConcurrentTrials": "int",
    "maxTotalTrials": "int",
    "timeout": "string",
    "trialTimeout": "string"
  },
  "objective": {
    "goal": "string",
    "primaryMetric": "string"
  },
  "outputs": {
    "{customized property}": {
      "description": "string",
      "jobOutputType": "string"
      // For remaining properties, see JobOutput objects
    }
  },
  "queueSettings": {
    "jobTier": "string",
    "priority": "int"
  },
  "samplingAlgorithm": {
    "samplingAlgorithmType": "string"
    // For remaining properties, see SamplingAlgorithm objects
  },
  "searchSpace": {},
  "trial": {
    "codeId": "string",
    "command": "string",
    "distribution": {
      "distributionType": "string"
      // For remaining properties, see DistributionConfiguration objects
    },
    "environmentId": "string",
    "environmentVariables": {
      "{customized property}": "string"
    },
    "resources": {
      "dockerArgs": "string",
      "instanceCount": "int",
      "instanceType": "string",
      "locations": [ "string" ],
      "maxInstanceCount": "int",
      "properties": {
        "{customized property}": {}
      },
      "shmSize": "string"
    }
  }

IdentityConfiguration objects

Set the identityType property to specify the type of object.

For AMLToken, use:

  "identityType": "AMLToken"

For Managed, use:

  "identityType": "Managed",
  "clientId": "string",
  "objectId": "string",
  "resourceId": "string"

For UserIdentity, use:

  "identityType": "UserIdentity"

Webhook objects

Set the webhookType property to specify the type of object.

For AzureDevOps, use:

  "webhookType": "AzureDevOps"

Nodes objects

Set the nodesValueType property to specify the type of object.

For All, use:

  "nodesValueType": "All"

JobOutput objects

Set the jobOutputType property to specify the type of object.

For custom_model, use:

  "jobOutputType": "custom_model",
  "assetName": "string",
  "assetVersion": "string",
  "autoDeleteSetting": {
    "condition": "string",
    "value": "string"
  },
  "mode": "string",
  "uri": "string"

For mlflow_model, use:

  "jobOutputType": "mlflow_model",
  "assetName": "string",
  "assetVersion": "string",
  "autoDeleteSetting": {
    "condition": "string",
    "value": "string"
  },
  "mode": "string",
  "uri": "string"

For mltable, use:

  "jobOutputType": "mltable",
  "assetName": "string",
  "assetVersion": "string",
  "autoDeleteSetting": {
    "condition": "string",
    "value": "string"
  },
  "mode": "string",
  "uri": "string"

For triton_model, use:

  "jobOutputType": "triton_model",
  "assetName": "string",
  "assetVersion": "string",
  "autoDeleteSetting": {
    "condition": "string",
    "value": "string"
  },
  "mode": "string",
  "uri": "string"

For uri_file, use:

  "jobOutputType": "uri_file",
  "assetName": "string",
  "assetVersion": "string",
  "autoDeleteSetting": {
    "condition": "string",
    "value": "string"
  },
  "mode": "string",
  "uri": "string"

For uri_folder, use:

  "jobOutputType": "uri_folder",
  "assetName": "string",
  "assetVersion": "string",
  "autoDeleteSetting": {
    "condition": "string",
    "value": "string"
  },
  "mode": "string",
  "uri": "string"

AutoMLVertical objects

Set the taskType property to specify the type of object.

For Classification, use:

  "taskType": "Classification",
  "cvSplitColumnNames": [ "string" ],
  "featurizationSettings": {
    "blockedTransformers": [ "string" ],
    "columnNameAndTypes": {
      "{customized property}": "string"
    },
    "datasetLanguage": "string",
    "enableDnnFeaturization": "bool",
    "mode": "string",
    "transformerParams": {
      "{customized property}": [
        {
          "fields": [ "string" ],
          "parameters": {}
        }
      ]
    }
  },
  "fixedParameters": {
    "booster": "string",
    "boostingType": "string",
    "growPolicy": "string",
    "learningRate": "int",
    "maxBin": "int",
    "maxDepth": "int",
    "maxLeaves": "int",
    "minDataInLeaf": "int",
    "minSplitGain": "int",
    "modelName": "string",
    "nEstimators": "int",
    "numLeaves": "int",
    "preprocessorName": "string",
    "regAlpha": "int",
    "regLambda": "int",
    "subsample": "int",
    "subsampleFreq": "int",
    "treeMethod": "string",
    "withMean": "bool",
    "withStd": "bool"
  },
  "limitSettings": {
    "enableEarlyTermination": "bool",
    "exitScore": "int",
    "maxConcurrentTrials": "int",
    "maxCoresPerTrial": "int",
    "maxNodes": "int",
    "maxTrials": "int",
    "sweepConcurrentTrials": "int",
    "sweepTrials": "int",
    "timeout": "string",
    "trialTimeout": "string"
  },
  "nCrossValidations": {
    "mode": "string"
    // For remaining properties, see NCrossValidations objects
  },
  "positiveLabel": "string",
  "primaryMetric": "string",
  "searchSpace": [
    {
      "booster": "string",
      "boostingType": "string",
      "growPolicy": "string",
      "learningRate": "string",
      "maxBin": "string",
      "maxDepth": "string",
      "maxLeaves": "string",
      "minDataInLeaf": "string",
      "minSplitGain": "string",
      "modelName": "string",
      "nEstimators": "string",
      "numLeaves": "string",
      "preprocessorName": "string",
      "regAlpha": "string",
      "regLambda": "string",
      "subsample": "string",
      "subsampleFreq": "string",
      "treeMethod": "string",
      "withMean": "string",
      "withStd": "string"
    }
  ],
  "sweepSettings": {
    "earlyTermination": {
      "delayEvaluation": "int",
      "evaluationInterval": "int",
      "policyType": "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    },
    "samplingAlgorithm": "string"
  },
  "testData": {
    "description": "string",
    "jobInputType": "string",
    "mode": "string",
    "uri": "string"
  },
  "testDataSize": "int",
  "trainingSettings": {
    "allowedTrainingAlgorithms": [ "string" ],
    "blockedTrainingAlgorithms": [ "string" ],
    "enableDnnTraining": "bool",
    "enableModelExplainability": "bool",
    "enableOnnxCompatibleModels": "bool",
    "enableStackEnsemble": "bool",
    "enableVoteEnsemble": "bool",
    "ensembleModelDownloadTimeout": "string",
    "stackEnsembleSettings": {
      "stackMetaLearnerKWargs": {},
      "stackMetaLearnerTrainPercentage": "int",
      "stackMetaLearnerType": "string"
    },
    "trainingMode": "string"
  },
  "validationData": {
    "description": "string",
    "jobInputType": "string",
    "mode": "string",
    "uri": "string"
  },
  "validationDataSize": "int",
  "weightColumnName": "string"

For Forecasting, use:

  "taskType": "Forecasting",
  "cvSplitColumnNames": [ "string" ],
  "featurizationSettings": {
    "blockedTransformers": [ "string" ],
    "columnNameAndTypes": {
      "{customized property}": "string"
    },
    "datasetLanguage": "string",
    "enableDnnFeaturization": "bool",
    "mode": "string",
    "transformerParams": {
      "{customized property}": [
        {
          "fields": [ "string" ],
          "parameters": {}
        }
      ]
    }
  },
  "fixedParameters": {
    "booster": "string",
    "boostingType": "string",
    "growPolicy": "string",
    "learningRate": "int",
    "maxBin": "int",
    "maxDepth": "int",
    "maxLeaves": "int",
    "minDataInLeaf": "int",
    "minSplitGain": "int",
    "modelName": "string",
    "nEstimators": "int",
    "numLeaves": "int",
    "preprocessorName": "string",
    "regAlpha": "int",
    "regLambda": "int",
    "subsample": "int",
    "subsampleFreq": "int",
    "treeMethod": "string",
    "withMean": "bool",
    "withStd": "bool"
  },
  "forecastingSettings": {
    "countryOrRegionForHolidays": "string",
    "cvStepSize": "int",
    "featureLags": "string",
    "featuresUnknownAtForecastTime": [ "string" ],
    "forecastHorizon": {
      "mode": "string"
      // For remaining properties, see ForecastHorizon objects
    },
    "frequency": "string",
    "seasonality": {
      "mode": "string"
      // For remaining properties, see Seasonality objects
    },
    "shortSeriesHandlingConfig": "string",
    "targetAggregateFunction": "string",
    "targetLags": {
      "mode": "string"
      // For remaining properties, see TargetLags objects
    },
    "targetRollingWindowSize": {
      "mode": "string"
      // For remaining properties, see TargetRollingWindowSize objects
    },
    "timeColumnName": "string",
    "timeSeriesIdColumnNames": [ "string" ],
    "useStl": "string"
  },
  "limitSettings": {
    "enableEarlyTermination": "bool",
    "exitScore": "int",
    "maxConcurrentTrials": "int",
    "maxCoresPerTrial": "int",
    "maxNodes": "int",
    "maxTrials": "int",
    "sweepConcurrentTrials": "int",
    "sweepTrials": "int",
    "timeout": "string",
    "trialTimeout": "string"
  },
  "nCrossValidations": {
    "mode": "string"
    // For remaining properties, see NCrossValidations objects
  },
  "primaryMetric": "string",
  "searchSpace": [
    {
      "booster": "string",
      "boostingType": "string",
      "growPolicy": "string",
      "learningRate": "string",
      "maxBin": "string",
      "maxDepth": "string",
      "maxLeaves": "string",
      "minDataInLeaf": "string",
      "minSplitGain": "string",
      "modelName": "string",
      "nEstimators": "string",
      "numLeaves": "string",
      "preprocessorName": "string",
      "regAlpha": "string",
      "regLambda": "string",
      "subsample": "string",
      "subsampleFreq": "string",
      "treeMethod": "string",
      "withMean": "string",
      "withStd": "string"
    }
  ],
  "sweepSettings": {
    "earlyTermination": {
      "delayEvaluation": "int",
      "evaluationInterval": "int",
      "policyType": "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    },
    "samplingAlgorithm": "string"
  },
  "testData": {
    "description": "string",
    "jobInputType": "string",
    "mode": "string",
    "uri": "string"
  },
  "testDataSize": "int",
  "trainingSettings": {
    "allowedTrainingAlgorithms": [ "string" ],
    "blockedTrainingAlgorithms": [ "string" ],
    "enableDnnTraining": "bool",
    "enableModelExplainability": "bool",
    "enableOnnxCompatibleModels": "bool",
    "enableStackEnsemble": "bool",
    "enableVoteEnsemble": "bool",
    "ensembleModelDownloadTimeout": "string",
    "stackEnsembleSettings": {
      "stackMetaLearnerKWargs": {},
      "stackMetaLearnerTrainPercentage": "int",
      "stackMetaLearnerType": "string"
    },
    "trainingMode": "string"
  },
  "validationData": {
    "description": "string",
    "jobInputType": "string",
    "mode": "string",
    "uri": "string"
  },
  "validationDataSize": "int",
  "weightColumnName": "string"

For ImageClassification, use:

  "taskType": "ImageClassification",
  "limitSettings": {
    "maxConcurrentTrials": "int",
    "maxTrials": "int",
    "timeout": "string"
  },
  "modelSettings": {
    "advancedSettings": "string",
    "amsGradient": "bool",
    "augmentations": "string",
    "beta1": "int",
    "beta2": "int",
    "checkpointFrequency": "int",
    "checkpointModel": {
      "description": "string",
      "jobInputType": "string",
      "mode": "string",
      "uri": "string"
    },
    "checkpointRunId": "string",
    "distributed": "bool",
    "earlyStopping": "bool",
    "earlyStoppingDelay": "int",
    "earlyStoppingPatience": "int",
    "enableOnnxNormalization": "bool",
    "evaluationFrequency": "int",
    "gradientAccumulationStep": "int",
    "layersToFreeze": "int",
    "learningRate": "int",
    "learningRateScheduler": "string",
    "modelName": "string",
    "momentum": "int",
    "nesterov": "bool",
    "numberOfEpochs": "int",
    "numberOfWorkers": "int",
    "optimizer": "string",
    "randomSeed": "int",
    "stepLRGamma": "int",
    "stepLRStepSize": "int",
    "trainingBatchSize": "int",
    "trainingCropSize": "int",
    "validationBatchSize": "int",
    "validationCropSize": "int",
    "validationResizeSize": "int",
    "warmupCosineLRCycles": "int",
    "warmupCosineLRWarmupEpochs": "int",
    "weightDecay": "int",
    "weightedLoss": "int"
  },
  "primaryMetric": "string",
  "searchSpace": [
    {
      "amsGradient": "string",
      "augmentations": "string",
      "beta1": "string",
      "beta2": "string",
      "distributed": "string",
      "earlyStopping": "string",
      "earlyStoppingDelay": "string",
      "earlyStoppingPatience": "string",
      "enableOnnxNormalization": "string",
      "evaluationFrequency": "string",
      "gradientAccumulationStep": "string",
      "layersToFreeze": "string",
      "learningRate": "string",
      "learningRateScheduler": "string",
      "modelName": "string",
      "momentum": "string",
      "nesterov": "string",
      "numberOfEpochs": "string",
      "numberOfWorkers": "string",
      "optimizer": "string",
      "randomSeed": "string",
      "stepLRGamma": "string",
      "stepLRStepSize": "string",
      "trainingBatchSize": "string",
      "trainingCropSize": "string",
      "validationBatchSize": "string",
      "validationCropSize": "string",
      "validationResizeSize": "string",
      "warmupCosineLRCycles": "string",
      "warmupCosineLRWarmupEpochs": "string",
      "weightDecay": "string",
      "weightedLoss": "string"
    }
  ],
  "sweepSettings": {
    "earlyTermination": {
      "delayEvaluation": "int",
      "evaluationInterval": "int",
      "policyType": "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    },
    "samplingAlgorithm": "string"
  },
  "validationData": {
    "description": "string",
    "jobInputType": "string",
    "mode": "string",
    "uri": "string"
  },
  "validationDataSize": "int"

For ImageClassificationMultilabel, use:

  "taskType": "ImageClassificationMultilabel",
  "limitSettings": {
    "maxConcurrentTrials": "int",
    "maxTrials": "int",
    "timeout": "string"
  },
  "modelSettings": {
    "advancedSettings": "string",
    "amsGradient": "bool",
    "augmentations": "string",
    "beta1": "int",
    "beta2": "int",
    "checkpointFrequency": "int",
    "checkpointModel": {
      "description": "string",
      "jobInputType": "string",
      "mode": "string",
      "uri": "string"
    },
    "checkpointRunId": "string",
    "distributed": "bool",
    "earlyStopping": "bool",
    "earlyStoppingDelay": "int",
    "earlyStoppingPatience": "int",
    "enableOnnxNormalization": "bool",
    "evaluationFrequency": "int",
    "gradientAccumulationStep": "int",
    "layersToFreeze": "int",
    "learningRate": "int",
    "learningRateScheduler": "string",
    "modelName": "string",
    "momentum": "int",
    "nesterov": "bool",
    "numberOfEpochs": "int",
    "numberOfWorkers": "int",
    "optimizer": "string",
    "randomSeed": "int",
    "stepLRGamma": "int",
    "stepLRStepSize": "int",
    "trainingBatchSize": "int",
    "trainingCropSize": "int",
    "validationBatchSize": "int",
    "validationCropSize": "int",
    "validationResizeSize": "int",
    "warmupCosineLRCycles": "int",
    "warmupCosineLRWarmupEpochs": "int",
    "weightDecay": "int",
    "weightedLoss": "int"
  },
  "primaryMetric": "string",
  "searchSpace": [
    {
      "amsGradient": "string",
      "augmentations": "string",
      "beta1": "string",
      "beta2": "string",
      "distributed": "string",
      "earlyStopping": "string",
      "earlyStoppingDelay": "string",
      "earlyStoppingPatience": "string",
      "enableOnnxNormalization": "string",
      "evaluationFrequency": "string",
      "gradientAccumulationStep": "string",
      "layersToFreeze": "string",
      "learningRate": "string",
      "learningRateScheduler": "string",
      "modelName": "string",
      "momentum": "string",
      "nesterov": "string",
      "numberOfEpochs": "string",
      "numberOfWorkers": "string",
      "optimizer": "string",
      "randomSeed": "string",
      "stepLRGamma": "string",
      "stepLRStepSize": "string",
      "trainingBatchSize": "string",
      "trainingCropSize": "string",
      "validationBatchSize": "string",
      "validationCropSize": "string",
      "validationResizeSize": "string",
      "warmupCosineLRCycles": "string",
      "warmupCosineLRWarmupEpochs": "string",
      "weightDecay": "string",
      "weightedLoss": "string"
    }
  ],
  "sweepSettings": {
    "earlyTermination": {
      "delayEvaluation": "int",
      "evaluationInterval": "int",
      "policyType": "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    },
    "samplingAlgorithm": "string"
  },
  "validationData": {
    "description": "string",
    "jobInputType": "string",
    "mode": "string",
    "uri": "string"
  },
  "validationDataSize": "int"

For ImageInstanceSegmentation, use:

  "taskType": "ImageInstanceSegmentation",
  "limitSettings": {
    "maxConcurrentTrials": "int",
    "maxTrials": "int",
    "timeout": "string"
  },
  "modelSettings": {
    "advancedSettings": "string",
    "amsGradient": "bool",
    "augmentations": "string",
    "beta1": "int",
    "beta2": "int",
    "boxDetectionsPerImage": "int",
    "boxScoreThreshold": "int",
    "checkpointFrequency": "int",
    "checkpointModel": {
      "description": "string",
      "jobInputType": "string",
      "mode": "string",
      "uri": "string"
    },
    "checkpointRunId": "string",
    "distributed": "bool",
    "earlyStopping": "bool",
    "earlyStoppingDelay": "int",
    "earlyStoppingPatience": "int",
    "enableOnnxNormalization": "bool",
    "evaluationFrequency": "int",
    "gradientAccumulationStep": "int",
    "imageSize": "int",
    "layersToFreeze": "int",
    "learningRate": "int",
    "learningRateScheduler": "string",
    "logTrainingMetrics": "string",
    "logValidationLoss": "string",
    "maxSize": "int",
    "minSize": "int",
    "modelName": "string",
    "modelSize": "string",
    "momentum": "int",
    "multiScale": "bool",
    "nesterov": "bool",
    "nmsIouThreshold": "int",
    "numberOfEpochs": "int",
    "numberOfWorkers": "int",
    "optimizer": "string",
    "randomSeed": "int",
    "stepLRGamma": "int",
    "stepLRStepSize": "int",
    "tileGridSize": "string",
    "tileOverlapRatio": "int",
    "tilePredictionsNmsThreshold": "int",
    "trainingBatchSize": "int",
    "validationBatchSize": "int",
    "validationIouThreshold": "int",
    "validationMetricType": "string",
    "warmupCosineLRCycles": "int",
    "warmupCosineLRWarmupEpochs": "int",
    "weightDecay": "int"
  },
  "primaryMetric": "MeanAveragePrecision",
  "searchSpace": [
    {
      "amsGradient": "string",
      "augmentations": "string",
      "beta1": "string",
      "beta2": "string",
      "boxDetectionsPerImage": "string",
      "boxScoreThreshold": "string",
      "distributed": "string",
      "earlyStopping": "string",
      "earlyStoppingDelay": "string",
      "earlyStoppingPatience": "string",
      "enableOnnxNormalization": "string",
      "evaluationFrequency": "string",
      "gradientAccumulationStep": "string",
      "imageSize": "string",
      "layersToFreeze": "string",
      "learningRate": "string",
      "learningRateScheduler": "string",
      "maxSize": "string",
      "minSize": "string",
      "modelName": "string",
      "modelSize": "string",
      "momentum": "string",
      "multiScale": "string",
      "nesterov": "string",
      "nmsIouThreshold": "string",
      "numberOfEpochs": "string",
      "numberOfWorkers": "string",
      "optimizer": "string",
      "randomSeed": "string",
      "stepLRGamma": "string",
      "stepLRStepSize": "string",
      "tileGridSize": "string",
      "tileOverlapRatio": "string",
      "tilePredictionsNmsThreshold": "string",
      "trainingBatchSize": "string",
      "validationBatchSize": "string",
      "validationIouThreshold": "string",
      "validationMetricType": "string",
      "warmupCosineLRCycles": "string",
      "warmupCosineLRWarmupEpochs": "string",
      "weightDecay": "string"
    }
  ],
  "sweepSettings": {
    "earlyTermination": {
      "delayEvaluation": "int",
      "evaluationInterval": "int",
      "policyType": "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    },
    "samplingAlgorithm": "string"
  },
  "validationData": {
    "description": "string",
    "jobInputType": "string",
    "mode": "string",
    "uri": "string"
  },
  "validationDataSize": "int"

For ImageObjectDetection, use:

  "taskType": "ImageObjectDetection",
  "limitSettings": {
    "maxConcurrentTrials": "int",
    "maxTrials": "int",
    "timeout": "string"
  },
  "modelSettings": {
    "advancedSettings": "string",
    "amsGradient": "bool",
    "augmentations": "string",
    "beta1": "int",
    "beta2": "int",
    "boxDetectionsPerImage": "int",
    "boxScoreThreshold": "int",
    "checkpointFrequency": "int",
    "checkpointModel": {
      "description": "string",
      "jobInputType": "string",
      "mode": "string",
      "uri": "string"
    },
    "checkpointRunId": "string",
    "distributed": "bool",
    "earlyStopping": "bool",
    "earlyStoppingDelay": "int",
    "earlyStoppingPatience": "int",
    "enableOnnxNormalization": "bool",
    "evaluationFrequency": "int",
    "gradientAccumulationStep": "int",
    "imageSize": "int",
    "layersToFreeze": "int",
    "learningRate": "int",
    "learningRateScheduler": "string",
    "logTrainingMetrics": "string",
    "logValidationLoss": "string",
    "maxSize": "int",
    "minSize": "int",
    "modelName": "string",
    "modelSize": "string",
    "momentum": "int",
    "multiScale": "bool",
    "nesterov": "bool",
    "nmsIouThreshold": "int",
    "numberOfEpochs": "int",
    "numberOfWorkers": "int",
    "optimizer": "string",
    "randomSeed": "int",
    "stepLRGamma": "int",
    "stepLRStepSize": "int",
    "tileGridSize": "string",
    "tileOverlapRatio": "int",
    "tilePredictionsNmsThreshold": "int",
    "trainingBatchSize": "int",
    "validationBatchSize": "int",
    "validationIouThreshold": "int",
    "validationMetricType": "string",
    "warmupCosineLRCycles": "int",
    "warmupCosineLRWarmupEpochs": "int",
    "weightDecay": "int"
  },
  "primaryMetric": "MeanAveragePrecision",
  "searchSpace": [
    {
      "amsGradient": "string",
      "augmentations": "string",
      "beta1": "string",
      "beta2": "string",
      "boxDetectionsPerImage": "string",
      "boxScoreThreshold": "string",
      "distributed": "string",
      "earlyStopping": "string",
      "earlyStoppingDelay": "string",
      "earlyStoppingPatience": "string",
      "enableOnnxNormalization": "string",
      "evaluationFrequency": "string",
      "gradientAccumulationStep": "string",
      "imageSize": "string",
      "layersToFreeze": "string",
      "learningRate": "string",
      "learningRateScheduler": "string",
      "maxSize": "string",
      "minSize": "string",
      "modelName": "string",
      "modelSize": "string",
      "momentum": "string",
      "multiScale": "string",
      "nesterov": "string",
      "nmsIouThreshold": "string",
      "numberOfEpochs": "string",
      "numberOfWorkers": "string",
      "optimizer": "string",
      "randomSeed": "string",
      "stepLRGamma": "string",
      "stepLRStepSize": "string",
      "tileGridSize": "string",
      "tileOverlapRatio": "string",
      "tilePredictionsNmsThreshold": "string",
      "trainingBatchSize": "string",
      "validationBatchSize": "string",
      "validationIouThreshold": "string",
      "validationMetricType": "string",
      "warmupCosineLRCycles": "string",
      "warmupCosineLRWarmupEpochs": "string",
      "weightDecay": "string"
    }
  ],
  "sweepSettings": {
    "earlyTermination": {
      "delayEvaluation": "int",
      "evaluationInterval": "int",
      "policyType": "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    },
    "samplingAlgorithm": "string"
  },
  "validationData": {
    "description": "string",
    "jobInputType": "string",
    "mode": "string",
    "uri": "string"
  },
  "validationDataSize": "int"

For Regression, use:

  "taskType": "Regression",
  "cvSplitColumnNames": [ "string" ],
  "featurizationSettings": {
    "blockedTransformers": [ "string" ],
    "columnNameAndTypes": {
      "{customized property}": "string"
    },
    "datasetLanguage": "string",
    "enableDnnFeaturization": "bool",
    "mode": "string",
    "transformerParams": {
      "{customized property}": [
        {
          "fields": [ "string" ],
          "parameters": {}
        }
      ]
    }
  },
  "fixedParameters": {
    "booster": "string",
    "boostingType": "string",
    "growPolicy": "string",
    "learningRate": "int",
    "maxBin": "int",
    "maxDepth": "int",
    "maxLeaves": "int",
    "minDataInLeaf": "int",
    "minSplitGain": "int",
    "modelName": "string",
    "nEstimators": "int",
    "numLeaves": "int",
    "preprocessorName": "string",
    "regAlpha": "int",
    "regLambda": "int",
    "subsample": "int",
    "subsampleFreq": "int",
    "treeMethod": "string",
    "withMean": "bool",
    "withStd": "bool"
  },
  "limitSettings": {
    "enableEarlyTermination": "bool",
    "exitScore": "int",
    "maxConcurrentTrials": "int",
    "maxCoresPerTrial": "int",
    "maxNodes": "int",
    "maxTrials": "int",
    "sweepConcurrentTrials": "int",
    "sweepTrials": "int",
    "timeout": "string",
    "trialTimeout": "string"
  },
  "nCrossValidations": {
    "mode": "string"
    // For remaining properties, see NCrossValidations objects
  },
  "primaryMetric": "string",
  "searchSpace": [
    {
      "booster": "string",
      "boostingType": "string",
      "growPolicy": "string",
      "learningRate": "string",
      "maxBin": "string",
      "maxDepth": "string",
      "maxLeaves": "string",
      "minDataInLeaf": "string",
      "minSplitGain": "string",
      "modelName": "string",
      "nEstimators": "string",
      "numLeaves": "string",
      "preprocessorName": "string",
      "regAlpha": "string",
      "regLambda": "string",
      "subsample": "string",
      "subsampleFreq": "string",
      "treeMethod": "string",
      "withMean": "string",
      "withStd": "string"
    }
  ],
  "sweepSettings": {
    "earlyTermination": {
      "delayEvaluation": "int",
      "evaluationInterval": "int",
      "policyType": "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    },
    "samplingAlgorithm": "string"
  },
  "testData": {
    "description": "string",
    "jobInputType": "string",
    "mode": "string",
    "uri": "string"
  },
  "testDataSize": "int",
  "trainingSettings": {
    "allowedTrainingAlgorithms": [ "string" ],
    "blockedTrainingAlgorithms": [ "string" ],
    "enableDnnTraining": "bool",
    "enableModelExplainability": "bool",
    "enableOnnxCompatibleModels": "bool",
    "enableStackEnsemble": "bool",
    "enableVoteEnsemble": "bool",
    "ensembleModelDownloadTimeout": "string",
    "stackEnsembleSettings": {
      "stackMetaLearnerKWargs": {},
      "stackMetaLearnerTrainPercentage": "int",
      "stackMetaLearnerType": "string"
    },
    "trainingMode": "string"
  },
  "validationData": {
    "description": "string",
    "jobInputType": "string",
    "mode": "string",
    "uri": "string"
  },
  "validationDataSize": "int",
  "weightColumnName": "string"

For TextClassification, use:

  "taskType": "TextClassification",
  "featurizationSettings": {
    "datasetLanguage": "string"
  },
  "fixedParameters": {
    "gradientAccumulationSteps": "int",
    "learningRate": "int",
    "learningRateScheduler": "string",
    "modelName": "string",
    "numberOfEpochs": "int",
    "trainingBatchSize": "int",
    "validationBatchSize": "int",
    "warmupRatio": "int",
    "weightDecay": "int"
  },
  "limitSettings": {
    "maxConcurrentTrials": "int",
    "maxNodes": "int",
    "maxTrials": "int",
    "timeout": "string",
    "trialTimeout": "string"
  },
  "primaryMetric": "string",
  "searchSpace": [
    {
      "gradientAccumulationSteps": "string",
      "learningRate": "string",
      "learningRateScheduler": "string",
      "modelName": "string",
      "numberOfEpochs": "string",
      "trainingBatchSize": "string",
      "validationBatchSize": "string",
      "warmupRatio": "string",
      "weightDecay": "string"
    }
  ],
  "sweepSettings": {
    "earlyTermination": {
      "delayEvaluation": "int",
      "evaluationInterval": "int",
      "policyType": "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    },
    "samplingAlgorithm": "string"
  },
  "validationData": {
    "description": "string",
    "jobInputType": "string",
    "mode": "string",
    "uri": "string"
  }

For TextClassificationMultilabel, use:

  "taskType": "TextClassificationMultilabel",
  "featurizationSettings": {
    "datasetLanguage": "string"
  },
  "fixedParameters": {
    "gradientAccumulationSteps": "int",
    "learningRate": "int",
    "learningRateScheduler": "string",
    "modelName": "string",
    "numberOfEpochs": "int",
    "trainingBatchSize": "int",
    "validationBatchSize": "int",
    "warmupRatio": "int",
    "weightDecay": "int"
  },
  "limitSettings": {
    "maxConcurrentTrials": "int",
    "maxNodes": "int",
    "maxTrials": "int",
    "timeout": "string",
    "trialTimeout": "string"
  },
  "searchSpace": [
    {
      "gradientAccumulationSteps": "string",
      "learningRate": "string",
      "learningRateScheduler": "string",
      "modelName": "string",
      "numberOfEpochs": "string",
      "trainingBatchSize": "string",
      "validationBatchSize": "string",
      "warmupRatio": "string",
      "weightDecay": "string"
    }
  ],
  "sweepSettings": {
    "earlyTermination": {
      "delayEvaluation": "int",
      "evaluationInterval": "int",
      "policyType": "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    },
    "samplingAlgorithm": "string"
  },
  "validationData": {
    "description": "string",
    "jobInputType": "string",
    "mode": "string",
    "uri": "string"
  }

For TextNER, use:

  "taskType": "TextNER",
  "featurizationSettings": {
    "datasetLanguage": "string"
  },
  "fixedParameters": {
    "gradientAccumulationSteps": "int",
    "learningRate": "int",
    "learningRateScheduler": "string",
    "modelName": "string",
    "numberOfEpochs": "int",
    "trainingBatchSize": "int",
    "validationBatchSize": "int",
    "warmupRatio": "int",
    "weightDecay": "int"
  },
  "limitSettings": {
    "maxConcurrentTrials": "int",
    "maxNodes": "int",
    "maxTrials": "int",
    "timeout": "string",
    "trialTimeout": "string"
  },
  "searchSpace": [
    {
      "gradientAccumulationSteps": "string",
      "learningRate": "string",
      "learningRateScheduler": "string",
      "modelName": "string",
      "numberOfEpochs": "string",
      "trainingBatchSize": "string",
      "validationBatchSize": "string",
      "warmupRatio": "string",
      "weightDecay": "string"
    }
  ],
  "sweepSettings": {
    "earlyTermination": {
      "delayEvaluation": "int",
      "evaluationInterval": "int",
      "policyType": "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    },
    "samplingAlgorithm": "string"
  },
  "validationData": {
    "description": "string",
    "jobInputType": "string",
    "mode": "string",
    "uri": "string"
  }

NCrossValidations objects

Set the mode property to specify the type of object.

For Auto, use:

  "mode": "Auto"

For Custom, use:

  "mode": "Custom",
  "value": "int"

EarlyTerminationPolicy objects

Set the policyType property to specify the type of object.

For Bandit, use:

  "policyType": "Bandit",
  "slackAmount": "int",
  "slackFactor": "int"

For MedianStopping, use:

  "policyType": "MedianStopping"

For TruncationSelection, use:

  "policyType": "TruncationSelection",
  "truncationPercentage": "int"

ForecastHorizon objects

Set the mode property to specify the type of object.

For Auto, use:

  "mode": "Auto"

For Custom, use:

  "mode": "Custom",
  "value": "int"

Seasonality objects

Set the mode property to specify the type of object.

For Auto, use:

  "mode": "Auto"

For Custom, use:

  "mode": "Custom",
  "value": "int"

TargetLags objects

Set the mode property to specify the type of object.

For Auto, use:

  "mode": "Auto"

For Custom, use:

  "mode": "Custom",
  "values": [ "int" ]

TargetRollingWindowSize objects

Set the mode property to specify the type of object.

For Auto, use:

  "mode": "Auto"

For Custom, use:

  "mode": "Custom",
  "value": "int"

DistributionConfiguration objects

Set the distributionType property to specify the type of object.

For Mpi, use:

  "distributionType": "Mpi",
  "processCountPerInstance": "int"

For PyTorch, use:

  "distributionType": "PyTorch",
  "processCountPerInstance": "int"

For Ray, use:

  "distributionType": "Ray",
  "address": "string",
  "dashboardPort": "int",
  "headNodeAdditionalArgs": "string",
  "includeDashboard": "bool",
  "port": "int",
  "workerNodeAdditionalArgs": "string"

For TensorFlow, use:

  "distributionType": "TensorFlow",
  "parameterServerCount": "int",
  "workerCount": "int"

JobInput objects

Set the jobInputType property to specify the type of object.

For custom_model, use:

  "jobInputType": "custom_model",
  "mode": "string",
  "uri": "string"

For literal, use:

  "jobInputType": "literal",
  "value": "string"

For mlflow_model, use:

  "jobInputType": "mlflow_model",
  "mode": "string",
  "uri": "string"

For mltable, use:

  "jobInputType": "mltable",
  "mode": "string",
  "uri": "string"

For triton_model, use:

  "jobInputType": "triton_model",
  "mode": "string",
  "uri": "string"

For uri_file, use:

  "jobInputType": "uri_file",
  "mode": "string",
  "uri": "string"

For uri_folder, use:

  "jobInputType": "uri_folder",
  "mode": "string",
  "uri": "string"

LabelingJobMediaProperties objects

Set the mediaType property to specify the type of object.

For Image, use:

  "mediaType": "Image",
  "annotationType": "string"

For Text, use:

  "mediaType": "Text",
  "annotationType": "string"

MLAssistConfiguration objects

Set the mlAssist property to specify the type of object.

For Disabled, use:

  "mlAssist": "Disabled"

For Enabled, use:

  "mlAssist": "Enabled",
  "inferencingComputeBinding": "string",
  "trainingComputeBinding": "string"

SparkJobEntry objects

Set the sparkJobEntryType property to specify the type of object.

For SparkJobPythonEntry, use:

  "sparkJobEntryType": "SparkJobPythonEntry",
  "file": "string"

For SparkJobScalaEntry, use:

  "sparkJobEntryType": "SparkJobScalaEntry",
  "className": "string"

SamplingAlgorithm objects

Set the samplingAlgorithmType property to specify the type of object.

For Bayesian, use:

  "samplingAlgorithmType": "Bayesian"

For Grid, use:

  "samplingAlgorithmType": "Grid"

For Random, use:

  "samplingAlgorithmType": "Random",
  "logbase": "string",
  "rule": "string",
  "seed": "int"

MonitoringAlertNotificationSettingsBase objects

Set the alertNotificationType property to specify the type of object.

For AzureMonitor, use:

  "alertNotificationType": "AzureMonitor"

For Email, use:

  "alertNotificationType": "Email",
  "emailNotificationSetting": {
    "emailOn": [ "string" ],
    "emails": [ "string" ],
    "webhooks": {
      "{customized property}": {
        "eventType": "string",
        "webhookType": "string"
        // For remaining properties, see Webhook objects
      }
    }
  }

MonitoringSignalBase objects

Set the signalType property to specify the type of object.

For Custom, use:

  "signalType": "Custom",
  "componentId": "string",
  "inputAssets": {
    "{customized property}": {
      "asset": {},
      "dataContext": "string",
      "preprocessingComponentId": "string",
      "targetColumnName": "string"
    }
  },
  "metricThresholds": [
    {
      "metric": "string",
      "threshold": {
        "value": "int"
      }
    }
  ]

For DataDrift, use:

  "signalType": "DataDrift",
  "baselineData": {
    "asset": {},
    "dataContext": "string",
    "preprocessingComponentId": "string",
    "targetColumnName": "string"
  },
  "dataSegment": {
    "feature": "string",
    "values": [ "string" ]
  },
  "features": {
    "filterType": "string"
    // For remaining properties, see MonitoringFeatureFilterBase objects
  },
  "metricThresholds": [
    {
      "threshold": {
        "value": "int"
      },
      "dataType": "string"
      // For remaining properties, see DataDriftMetricThresholdBase objects
    }
  ],
  "targetData": {
    "asset": {},
    "dataContext": "string",
    "preprocessingComponentId": "string",
    "targetColumnName": "string"
  }

For DataQuality, use:

  "signalType": "DataQuality",
  "baselineData": {
    "asset": {},
    "dataContext": "string",
    "preprocessingComponentId": "string",
    "targetColumnName": "string"
  },
  "features": {
    "filterType": "string"
    // For remaining properties, see MonitoringFeatureFilterBase objects
  },
  "metricThresholds": [
    {
      "threshold": {
        "value": "int"
      },
      "dataType": "string"
      // For remaining properties, see DataQualityMetricThresholdBase objects
    }
  ],
  "targetData": {
    "asset": {},
    "dataContext": "string",
    "preprocessingComponentId": "string",
    "targetColumnName": "string"
  }

For FeatureAttributionDrift, use:

  "signalType": "FeatureAttributionDrift",
  "baselineData": {
    "asset": {},
    "dataContext": "string",
    "preprocessingComponentId": "string",
    "targetColumnName": "string"
  },
  "metricThreshold": {
    "metric": "NormalizedDiscountedCumulativeGain",
    "threshold": {
      "value": "int"
    }
  },
  "modelType": "string",
  "targetData": {
    "asset": {},
    "dataContext": "string",
    "preprocessingComponentId": "string",
    "targetColumnName": "string"
  }

For ModelPerformance, use:

  "signalType": "ModelPerformance",
  "baselineData": {
    "asset": {},
    "dataContext": "string",
    "preprocessingComponentId": "string",
    "targetColumnName": "string"
  },
  "dataSegment": {
    "feature": "string",
    "values": [ "string" ]
  },
  "metricThreshold": {
    "threshold": {
      "value": "int"
    },
    "modelType": "string"
    // For remaining properties, see ModelPerformanceMetricThresholdBase objects
  },
  "targetData": {
    "asset": {},
    "dataContext": "string",
    "preprocessingComponentId": "string",
    "targetColumnName": "string"
  }

For PredictionDrift, use:

  "signalType": "PredictionDrift",
  "baselineData": {
    "asset": {},
    "dataContext": "string",
    "preprocessingComponentId": "string",
    "targetColumnName": "string"
  },
  "metricThresholds": [
    {
      "threshold": {
        "value": "int"
      },
      "dataType": "string"
      // For remaining properties, see PredictionDriftMetricThresholdBase objects
    }
  ],
  "modelType": "string",
  "targetData": {
    "asset": {},
    "dataContext": "string",
    "preprocessingComponentId": "string",
    "targetColumnName": "string"
  }

MonitoringFeatureFilterBase objects

Set the filterType property to specify the type of object.

For AllFeatures, use:

  "filterType": "AllFeatures"

For FeatureSubset, use:

  "filterType": "FeatureSubset",
  "features": [ "string" ]

For TopNByAttribution, use:

  "filterType": "TopNByAttribution",
  "top": "int"

DataDriftMetricThresholdBase objects

Set the dataType property to specify the type of object.

For Categorical, use:

  "dataType": "Categorical",
  "metric": "string"

For Numerical, use:

  "dataType": "Numerical",
  "metric": "string"

DataQualityMetricThresholdBase objects

Set the dataType property to specify the type of object.

For Categorical, use:

  "dataType": "Categorical",
  "metric": "string"

For Numerical, use:

  "dataType": "Numerical",
  "metric": "string"

ModelPerformanceMetricThresholdBase objects

Set the modelType property to specify the type of object.

For Classification, use:

  "modelType": "Classification",
  "metric": "string"

For Regression, use:

  "modelType": "Regression",
  "metric": "string"

PredictionDriftMetricThresholdBase objects

Set the dataType property to specify the type of object.

For Categorical, use:

  "dataType": "Categorical",
  "metric": "string"

For Numerical, use:

  "dataType": "Numerical",
  "metric": "string"

DataImportSource objects

Set the sourceType property to specify the type of object.

For database, use:

  "sourceType": "database",
  "query": "string",
  "storedProcedure": "string",
  "storedProcedureParams": [
    {
      "{customized property}": "string"
    }
  ],
  "tableName": "string"

For file_system, use:

  "sourceType": "file_system",
  "path": "string"

TriggerBase objects

Set the triggerType property to specify the type of object.

For Cron, use:

  "triggerType": "Cron",
  "expression": "string"

For Recurrence, use:

  "triggerType": "Recurrence",
  "frequency": "string",
  "interval": "int",
  "schedule": {
    "hours": [ "int" ],
    "minutes": [ "int" ],
    "monthDays": [ "int" ],
    "weekDays": [ "string" ]
  }

Property values

workspaces/schedules

Name Description Value
type The resource type 'Microsoft.MachineLearningServices/workspaces/schedules'
apiVersion The resource api version '2023-04-01-preview'
name The resource name

See how to set names and types for child resources in JSON ARM templates.		 string (required)
properties [Required] Additional attributes of the entity. ScheduleProperties (required)

ScheduleProperties

Name Description Value
action [Required] Specifies the action of the schedule ScheduleActionBase (required)
description The asset description text. string
displayName Display name of schedule. string
isEnabled Is the schedule enabled? bool
properties The asset property dictionary. ResourceBaseProperties
tags Tag dictionary. Tags can be added, removed, and updated. object
trigger [Required] Specifies the trigger details TriggerBase (required)

ScheduleActionBase

Name Description Value
actionType Set the object type CreateJob
CreateMonitor
ImportData
InvokeBatchEndpoint (required)

JobScheduleAction

Name Description Value
actionType [Required] Specifies the action type of the schedule 'CreateJob' (required)
jobDefinition [Required] Defines Schedule action definition details. JobBaseProperties (required)

JobBaseProperties

Name Description Value
componentId ARM resource ID of the component resource. string
computeId ARM resource ID of the compute resource. string
description The asset description text. string
displayName Display name of job. string
experimentName The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment. string
identity Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null.
Defaults to AmlToken if null.		 IdentityConfiguration
isArchived Is the asset archived? bool
notificationSetting Notification setting for the job NotificationSetting
properties The asset property dictionary. ResourceBaseProperties
secretsConfiguration Configuration for secrets to be made available during runtime. JobBaseSecretsConfiguration
services List of JobEndpoints.
For local jobs, a job endpoint will have an endpoint value of FileStreamObject.		 JobBaseServices
tags Tag dictionary. Tags can be added, removed, and updated. object
jobType Set the object type AutoML
Command
Labeling
Pipeline
Spark
Sweep (required)

IdentityConfiguration

Name Description Value
identityType Set the object type AMLToken
Managed
UserIdentity (required)

AmlToken

Name Description Value
identityType [Required] Specifies the type of identity framework. 'AMLToken' (required)

ManagedIdentity

Name Description Value
identityType [Required] Specifies the type of identity framework. 'Managed' (required)
clientId Specifies a user-assigned identity by client ID. For system-assigned, do not set this field. string

Constraints:
Min length = 36
Max length = 36
Pattern = ^[0-9a-fA-F]{8}-([0-9a-fA-F]{4}-){3}[0-9a-fA-F]{12}$
objectId Specifies a user-assigned identity by object ID. For system-assigned, do not set this field. string

Constraints:
Min length = 36
Max length = 36
Pattern = ^[0-9a-fA-F]{8}-([0-9a-fA-F]{4}-){3}[0-9a-fA-F]{12}$
resourceId Specifies a user-assigned identity by ARM resource ID. For system-assigned, do not set this field. string

UserIdentity

Name Description Value
identityType [Required] Specifies the type of identity framework. 'UserIdentity' (required)

NotificationSetting

Name Description Value
emailOn Send email notification to user on specified notification type String array containing any of:
'JobCancelled'
'JobCompleted'
'JobFailed'
emails This is the email recipient list which has a limitation of 499 characters in total concat with comma separator string[]
webhooks Send webhook callback to a service. Key is a user-provided name for the webhook. NotificationSettingWebhooks

NotificationSettingWebhooks

Name Description Value
{customized property} Webhook

Webhook

Name Description Value
eventType Send callback on a specified notification event string
webhookType Set the object type AzureDevOps (required)

AzureDevOpsWebhook

Name Description Value
webhookType [Required] Specifies the type of service to send a callback 'AzureDevOps' (required)

ResourceBaseProperties

Name Description Value
{customized property} string

JobBaseSecretsConfiguration

Name Description Value
{customized property} SecretConfiguration

SecretConfiguration

Name Description Value
uri Secret Uri.
Sample Uri : https://myvault.vault.azure.net/secrets/mysecretname/secretversion		 string
workspaceSecretName Name of secret in workspace key vault. string

JobBaseServices

Name Description Value
{customized property} JobService

JobService

Name Description Value
endpoint Url for endpoint. string
jobServiceType Endpoint type. string
nodes Nodes that user would like to start the service on.
If Nodes is not set or set to null, the service will only be started on leader node.		 Nodes
port Port for endpoint set by user. int
properties Additional properties to set on the endpoint. JobServiceProperties

Nodes

Name Description Value
nodesValueType Set the object type All (required)

AllNodes

Name Description Value
nodesValueType [Required] Type of the Nodes value 'All' (required)

JobServiceProperties

Name Description Value
{customized property} string

AutoMLJob

Name Description Value
jobType [Required] Specifies the type of job. 'AutoML' (required)
environmentId The ARM resource ID of the Environment specification for the job.
This is optional value to provide, if not provided, AutoML will default this to Production AutoML curated environment version when running the job.		 string
environmentVariables Environment variables included in the job. AutoMLJobEnvironmentVariables
outputs Mapping of output data bindings used in the job. AutoMLJobOutputs
queueSettings Queue settings for the job QueueSettings
resources Compute Resource configuration for the job. JobResourceConfiguration
taskDetails [Required] This represents scenario which can be one of Tables/NLP/Image AutoMLVertical (required)

AutoMLJobEnvironmentVariables

Name Description Value
{customized property} string

AutoMLJobOutputs

Name Description Value
{customized property} JobOutput

JobOutput

Name Description Value
description Description for the output. string
jobOutputType Set the object type custom_model
mlflow_model
mltable
triton_model
uri_file
uri_folder (required)

CustomModelJobOutput

Name Description Value
jobOutputType [Required] Specifies the type of job. 'custom_model' (required)
assetName Output Asset Name. string
assetVersion Output Asset Version. string
autoDeleteSetting Auto delete setting of output data asset. AutoDeleteSetting
mode Output Asset Delivery Mode. 'Direct'
'ReadWriteMount'
'Upload'
uri Output Asset URI. string

AutoDeleteSetting

Name Description Value
condition When to check if an asset is expired 'CreatedGreaterThan'
'LastAccessedGreaterThan'
value Expiration condition value. string

MLFlowModelJobOutput

Name Description Value
jobOutputType [Required] Specifies the type of job. 'mlflow_model' (required)
assetName Output Asset Name. string
assetVersion Output Asset Version. string
autoDeleteSetting Auto delete setting of output data asset. AutoDeleteSetting
mode Output Asset Delivery Mode. 'Direct'
'ReadWriteMount'
'Upload'
uri Output Asset URI. string

MLTableJobOutput

Name Description Value
jobOutputType [Required] Specifies the type of job. 'mltable' (required)
assetName Output Asset Name. string
assetVersion Output Asset Version. string
autoDeleteSetting Auto delete setting of output data asset. AutoDeleteSetting
mode Output Asset Delivery Mode. 'Direct'
'ReadWriteMount'
'Upload'
uri Output Asset URI. string

TritonModelJobOutput

Name Description Value
jobOutputType [Required] Specifies the type of job. 'triton_model' (required)
assetName Output Asset Name. string
assetVersion Output Asset Version. string
autoDeleteSetting Auto delete setting of output data asset. AutoDeleteSetting
mode Output Asset Delivery Mode. 'Direct'
'ReadWriteMount'
'Upload'
uri Output Asset URI. string

UriFileJobOutput

Name Description Value
jobOutputType [Required] Specifies the type of job. 'uri_file' (required)
assetName Output Asset Name. string
assetVersion Output Asset Version. string
autoDeleteSetting Auto delete setting of output data asset. AutoDeleteSetting
mode Output Asset Delivery Mode. 'Direct'
'ReadWriteMount'
'Upload'
uri Output Asset URI. string

UriFolderJobOutput

Name Description Value
jobOutputType [Required] Specifies the type of job. 'uri_folder' (required)
assetName Output Asset Name. string
assetVersion Output Asset Version. string
autoDeleteSetting Auto delete setting of output data asset. AutoDeleteSetting
mode Output Asset Delivery Mode. 'Direct'
'ReadWriteMount'
'Upload'
uri Output Asset URI. string

QueueSettings

Name Description Value
jobTier Enum to determine the job tier. 'Basic'
'Premium'
'Spot'
'Standard'
priority Controls the priority of the job on a compute. int

JobResourceConfiguration

Name Description Value
dockerArgs Extra arguments to pass to the Docker run command. This would override any parameters that have already been set by the system, or in this section. This parameter is only supported for Azure ML compute types. string
instanceCount Optional number of instances or nodes used by the compute target. int
instanceType Optional type of VM used as supported by the compute target. string
locations Locations where the job can run. string[]
maxInstanceCount Optional max allowed number of instances or nodes to be used by the compute target.
For use with elastic training, currently supported by PyTorch distribution type only.		 int
properties Additional properties bag. ResourceConfigurationProperties
shmSize Size of the docker container's shared memory block. This should be in the format of (number)(unit) where number as to be greater than 0 and the unit can be one of b(bytes), k(kilobytes), m(megabytes), or g(gigabytes). string

Constraints:
Pattern = \d+[bBkKmMgG]

ResourceConfigurationProperties

Name Description Value
{customized property}

AutoMLVertical

Name Description Value
logVerbosity Log verbosity for the job. 'Critical'
'Debug'
'Error'
'Info'
'NotSet'
'Warning'
targetColumnName Target column name: This is prediction values column.
Also known as label column name in context of classification tasks.		 string
trainingData [Required] Training data input. MLTableJobInput (required)
taskType Set the object type Classification
Forecasting
ImageClassification
ImageClassificationMultilabel
ImageInstanceSegmentation
ImageObjectDetection
Regression
TextClassification
TextClassificationMultilabel
TextNER (required)

MLTableJobInput

Name Description Value
description Description for the input. string
jobInputType [Required] Specifies the type of job. 'custom_model'
'literal'
'mlflow_model'
'mltable'
'triton_model'
'uri_file'
'uri_folder' (required)
mode Input Asset Delivery Mode. 'Direct'
'Download'
'EvalDownload'
'EvalMount'
'ReadOnlyMount'
'ReadWriteMount'
uri [Required] Input Asset URI. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]

Classification

Name Description Value
taskType [Required] Task type for AutoMLJob. 'Classification' (required)
cvSplitColumnNames Columns to use for CVSplit data. string[]
featurizationSettings Featurization inputs needed for AutoML job. TableVerticalFeaturizationSettings
fixedParameters Model/training parameters that will remain constant throughout training. TableFixedParameters
limitSettings Execution constraints for AutoMLJob. TableVerticalLimitSettings
nCrossValidations Number of cross validation folds to be applied on training dataset
when validation dataset is not provided.		 NCrossValidations
positiveLabel Positive label for binary metrics calculation. string
primaryMetric Primary metric for the task. 'AUCWeighted'
'Accuracy'
'AveragePrecisionScoreWeighted'
'NormMacroRecall'
'PrecisionScoreWeighted'
searchSpace Search space for sampling different combinations of models and their hyperparameters. TableParameterSubspace[]
sweepSettings Settings for model sweeping and hyperparameter tuning. TableSweepSettings
testData Test data input. MLTableJobInput
testDataSize The fraction of test dataset that needs to be set aside for validation purpose.
Values between (0.0 , 1.0)
Applied when validation dataset is not provided.		 int
trainingSettings Inputs for training phase for an AutoML Job. ClassificationTrainingSettings
validationData Validation data inputs. MLTableJobInput
validationDataSize The fraction of training dataset that needs to be set aside for validation purpose.
Values between (0.0 , 1.0)
Applied when validation dataset is not provided.		 int
weightColumnName The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down. string

TableVerticalFeaturizationSettings

Name Description Value
blockedTransformers These transformers shall not be used in featurization. String array containing any of:
'CatTargetEncoder'
'CountVectorizer'
'HashOneHotEncoder'
'LabelEncoder'
'NaiveBayes'
'OneHotEncoder'
'TextTargetEncoder'
'TfIdf'
'WoETargetEncoder'
'WordEmbedding'
columnNameAndTypes Dictionary of column name and its type (int, float, string, datetime etc). TableVerticalFeaturizationSettingsColumnNameAndTypes
datasetLanguage Dataset language, useful for the text data. string
enableDnnFeaturization Determines whether to use Dnn based featurizers for data featurization. bool
mode Featurization mode - User can keep the default 'Auto' mode and AutoML will take care of necessary transformation of the data in featurization phase.
If 'Off' is selected then no featurization is done.
If 'Custom' is selected then user can specify additional inputs to customize how featurization is done.		 'Auto'
'Custom'
'Off'
transformerParams User can specify additional transformers to be used along with the columns to which it would be applied and parameters for the transformer constructor. TableVerticalFeaturizationSettingsTransformerParams

TableVerticalFeaturizationSettingsColumnNameAndTypes

Name Description Value
{customized property} string

TableVerticalFeaturizationSettingsTransformerParams

Name Description Value
{customized property} ColumnTransformer[]

ColumnTransformer

Name Description Value
fields Fields to apply transformer logic on. string[]
parameters Different properties to be passed to transformer.
Input expected is dictionary of key,value pairs in JSON format.

TableFixedParameters

Name Description Value
booster Specify the boosting type, e.g gbdt for XGBoost. string
boostingType Specify the boosting type, e.g gbdt for LightGBM. string
growPolicy Specify the grow policy, which controls the way new nodes are added to the tree. string
learningRate The learning rate for the training procedure. int
maxBin Specify the Maximum number of discrete bins to bucket continuous features . int
maxDepth Specify the max depth to limit the tree depth explicitly. int
maxLeaves Specify the max leaves to limit the tree leaves explicitly. int
minDataInLeaf The minimum number of data per leaf. int
minSplitGain Minimum loss reduction required to make a further partition on a leaf node of the tree. int
modelName The name of the model to train. string
nEstimators Specify the number of trees (or rounds) in an model. int
numLeaves Specify the number of leaves. int
preprocessorName The name of the preprocessor to use. string
regAlpha L1 regularization term on weights. int
regLambda L2 regularization term on weights. int
subsample Subsample ratio of the training instance. int
subsampleFreq Frequency of subsample. int
treeMethod Specify the tree method. string
withMean If true, center before scaling the data with StandardScalar. bool
withStd If true, scaling the data with Unit Variance with StandardScalar. bool

TableVerticalLimitSettings

Name Description Value
enableEarlyTermination Enable early termination, determines whether or not if AutoMLJob will terminate early if there is no score improvement in last 20 iterations. bool
exitScore Exit score for the AutoML job. int
maxConcurrentTrials Maximum Concurrent iterations. int
maxCoresPerTrial Max cores per iteration. int
maxNodes Maximum nodes to use for the experiment. int
maxTrials Number of iterations. int
sweepConcurrentTrials Number of concurrent sweeping runs that user wants to trigger. int
sweepTrials Number of sweeping runs that user wants to trigger. int
timeout AutoML job timeout. string
trialTimeout Iteration timeout. string

NCrossValidations

Name Description Value
mode Set the object type Auto
Custom (required)

AutoNCrossValidations

Name Description Value
mode [Required] Mode for determining N-Cross validations. 'Auto' (required)

CustomNCrossValidations

Name Description Value
mode [Required] Mode for determining N-Cross validations. 'Custom' (required)
value [Required] N-Cross validations value. int (required)

TableParameterSubspace

Name Description Value
booster Specify the boosting type, e.g gbdt for XGBoost. string
boostingType Specify the boosting type, e.g gbdt for LightGBM. string
growPolicy Specify the grow policy, which controls the way new nodes are added to the tree. string
learningRate The learning rate for the training procedure. string
maxBin Specify the Maximum number of discrete bins to bucket continuous features . string
maxDepth Specify the max depth to limit the tree depth explicitly. string
maxLeaves Specify the max leaves to limit the tree leaves explicitly. string
minDataInLeaf The minimum number of data per leaf. string
minSplitGain Minimum loss reduction required to make a further partition on a leaf node of the tree. string
modelName The name of the model to train. string
nEstimators Specify the number of trees (or rounds) in an model. string
numLeaves Specify the number of leaves. string
preprocessorName The name of the preprocessor to use. string
regAlpha L1 regularization term on weights. string
regLambda L2 regularization term on weights. string
subsample Subsample ratio of the training instance. string
subsampleFreq Frequency of subsample string
treeMethod Specify the tree method. string
withMean If true, center before scaling the data with StandardScalar. string
withStd If true, scaling the data with Unit Variance with StandardScalar. string

TableSweepSettings

Name Description Value
earlyTermination Type of early termination policy for the sweeping job. EarlyTerminationPolicy
samplingAlgorithm [Required] Type of sampling algorithm. 'Bayesian'
'Grid'
'Random' (required)

EarlyTerminationPolicy

Name Description Value
delayEvaluation Number of intervals by which to delay the first evaluation. int
evaluationInterval Interval (number of runs) between policy evaluations. int
policyType Set the object type Bandit
MedianStopping
TruncationSelection (required)

BanditPolicy

Name Description Value
policyType [Required] Name of policy configuration 'Bandit' (required)
slackAmount Absolute distance allowed from the best performing run. int
slackFactor Ratio of the allowed distance from the best performing run. int

MedianStoppingPolicy

Name Description Value
policyType [Required] Name of policy configuration 'MedianStopping' (required)

TruncationSelectionPolicy

Name Description Value
policyType [Required] Name of policy configuration 'TruncationSelection' (required)
truncationPercentage The percentage of runs to cancel at each evaluation interval. int

ClassificationTrainingSettings

Name Description Value
allowedTrainingAlgorithms Allowed models for classification task. String array containing any of:
'BernoulliNaiveBayes'
'DecisionTree'
'ExtremeRandomTrees'
'GradientBoosting'
'KNN'
'LightGBM'
'LinearSVM'
'LogisticRegression'
'MultinomialNaiveBayes'
'RandomForest'
'SGD'
'SVM'
'XGBoostClassifier'
blockedTrainingAlgorithms Blocked models for classification task. String array containing any of:
'BernoulliNaiveBayes'
'DecisionTree'
'ExtremeRandomTrees'
'GradientBoosting'
'KNN'
'LightGBM'
'LinearSVM'
'LogisticRegression'
'MultinomialNaiveBayes'
'RandomForest'
'SGD'
'SVM'
'XGBoostClassifier'
enableDnnTraining Enable recommendation of DNN models. bool
enableModelExplainability Flag to turn on explainability on best model. bool
enableOnnxCompatibleModels Flag for enabling onnx compatible models. bool
enableStackEnsemble Enable stack ensemble run. bool
enableVoteEnsemble Enable voting ensemble run. bool
ensembleModelDownloadTimeout During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded.
Configure this parameter with a higher value than 300 secs, if more time is needed.		 string
stackEnsembleSettings Stack ensemble settings for stack ensemble run. StackEnsembleSettings
trainingMode TrainingMode mode - Setting to 'auto' is same as setting it to 'non-distributed' for now, however in the future may result in mixed mode or heuristics based mode selection. Default is 'auto'.
If 'Distributed' then only distributed featurization is used and distributed algorithms are chosen.
If 'NonDistributed' then only non distributed algorithms are chosen.
 'Auto'
'Distributed'
'NonDistributed'

StackEnsembleSettings

Name Description Value
stackMetaLearnerKWargs Optional parameters to pass to the initializer of the meta-learner.
stackMetaLearnerTrainPercentage Specifies the proportion of the training set (when choosing train and validation type of training) to be reserved for training the meta-learner. Default value is 0.2. int
stackMetaLearnerType The meta-learner is a model trained on the output of the individual heterogeneous models. 'ElasticNet'
'ElasticNetCV'
'LightGBMClassifier'
'LightGBMRegressor'
'LinearRegression'
'LogisticRegression'
'LogisticRegressionCV'
'None'

Forecasting

Name Description Value
taskType [Required] Task type for AutoMLJob. 'Forecasting' (required)
cvSplitColumnNames Columns to use for CVSplit data. string[]
featurizationSettings Featurization inputs needed for AutoML job. TableVerticalFeaturizationSettings
fixedParameters Model/training parameters that will remain constant throughout training. TableFixedParameters
forecastingSettings Forecasting task specific inputs. ForecastingSettings
limitSettings Execution constraints for AutoMLJob. TableVerticalLimitSettings
nCrossValidations Number of cross validation folds to be applied on training dataset
when validation dataset is not provided.		 NCrossValidations
primaryMetric Primary metric for forecasting task. 'NormalizedMeanAbsoluteError'
'NormalizedRootMeanSquaredError'
'R2Score'
'SpearmanCorrelation'
searchSpace Search space for sampling different combinations of models and their hyperparameters. TableParameterSubspace[]
sweepSettings Settings for model sweeping and hyperparameter tuning. TableSweepSettings
testData Test data input. MLTableJobInput
testDataSize The fraction of test dataset that needs to be set aside for validation purpose.
Values between (0.0 , 1.0)
Applied when validation dataset is not provided.		 int
trainingSettings Inputs for training phase for an AutoML Job. ForecastingTrainingSettings
validationData Validation data inputs. MLTableJobInput
validationDataSize The fraction of training dataset that needs to be set aside for validation purpose.
Values between (0.0 , 1.0)
Applied when validation dataset is not provided.		 int
weightColumnName The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down. string

ForecastingSettings

Name Description Value
countryOrRegionForHolidays Country or region for holidays for forecasting tasks.
These should be ISO 3166 two-letter country/region codes, for example 'US' or 'GB'.		 string
cvStepSize Number of periods between the origin time of one CV fold and the next fold. For
example, if CVStepSize = 3 for daily data, the origin time for each fold will be
three days apart.		 int
featureLags Flag for generating lags for the numeric features with 'auto' or null. 'Auto'
'None'
featuresUnknownAtForecastTime The feature columns that are available for training but unknown at the time of forecast/inference.
If features_unknown_at_forecast_time is not set, it is assumed that all the feature columns in the dataset are known at inference time.		 string[]
forecastHorizon The desired maximum forecast horizon in units of time-series frequency. ForecastHorizon
frequency When forecasting, this parameter represents the period with which the forecast is desired, for example daily, weekly, yearly, etc. The forecast frequency is dataset frequency by default. string
seasonality Set time series seasonality as an integer multiple of the series frequency.
If seasonality is set to 'auto', it will be inferred.		 Seasonality
shortSeriesHandlingConfig The parameter defining how if AutoML should handle short time series. 'Auto'
'Drop'
'None'
'Pad'
targetAggregateFunction The function to be used to aggregate the time series target column to conform to a user specified frequency.
If the TargetAggregateFunction is set i.e. not 'None', but the freq parameter is not set, the error is raised. The possible target aggregation functions are: "sum", "max", "min" and "mean".		 'Max'
'Mean'
'Min'
'None'
'Sum'
targetLags The number of past periods to lag from the target column. TargetLags
targetRollingWindowSize The number of past periods used to create a rolling window average of the target column. TargetRollingWindowSize
timeColumnName The name of the time column. This parameter is required when forecasting to specify the datetime column in the input data used for building the time series and inferring its frequency. string
timeSeriesIdColumnNames The names of columns used to group a timeseries. It can be used to create multiple series.
If grain is not defined, the data set is assumed to be one time-series. This parameter is used with task type forecasting.		 string[]
useStl Configure STL Decomposition of the time-series target column. 'None'
'Season'
'SeasonTrend'

ForecastHorizon

Name Description Value
mode Set the object type Auto
Custom (required)

AutoForecastHorizon

Name Description Value
mode [Required] Set forecast horizon value selection mode. 'Auto' (required)

CustomForecastHorizon

Name Description Value
mode [Required] Set forecast horizon value selection mode. 'Custom' (required)
value [Required] Forecast horizon value. int (required)

Seasonality

Name Description Value
mode Set the object type Auto
Custom (required)

AutoSeasonality

Name Description Value
mode [Required] Seasonality mode. 'Auto' (required)

CustomSeasonality

Name Description Value
mode [Required] Seasonality mode. 'Custom' (required)
value [Required] Seasonality value. int (required)

TargetLags

Name Description Value
mode Set the object type Auto
Custom (required)

AutoTargetLags

Name Description Value
mode [Required] Set target lags mode - Auto/Custom 'Auto' (required)

CustomTargetLags

Name Description Value
mode [Required] Set target lags mode - Auto/Custom 'Custom' (required)
values [Required] Set target lags values. int[] (required)

TargetRollingWindowSize

Name Description Value
mode Set the object type Auto
Custom (required)

AutoTargetRollingWindowSize

Name Description Value
mode [Required] TargetRollingWindowSiz detection mode. 'Auto' (required)

CustomTargetRollingWindowSize

Name Description Value
mode [Required] TargetRollingWindowSiz detection mode. 'Custom' (required)
value [Required] TargetRollingWindowSize value. int (required)

ForecastingTrainingSettings

Name Description Value
allowedTrainingAlgorithms Allowed models for forecasting task. String array containing any of:
'Arimax'
'AutoArima'
'Average'
'DecisionTree'
'ElasticNet'
'ExponentialSmoothing'
'ExtremeRandomTrees'
'GradientBoosting'
'KNN'
'LassoLars'
'LightGBM'
'Naive'
'Prophet'
'RandomForest'
'SGD'
'SeasonalAverage'
'SeasonalNaive'
'TCNForecaster'
'XGBoostRegressor'
blockedTrainingAlgorithms Blocked models for forecasting task. String array containing any of:
'Arimax'
'AutoArima'
'Average'
'DecisionTree'
'ElasticNet'
'ExponentialSmoothing'
'ExtremeRandomTrees'
'GradientBoosting'
'KNN'
'LassoLars'
'LightGBM'
'Naive'
'Prophet'
'RandomForest'
'SGD'
'SeasonalAverage'
'SeasonalNaive'
'TCNForecaster'
'XGBoostRegressor'
enableDnnTraining Enable recommendation of DNN models. bool
enableModelExplainability Flag to turn on explainability on best model. bool
enableOnnxCompatibleModels Flag for enabling onnx compatible models. bool
enableStackEnsemble Enable stack ensemble run. bool
enableVoteEnsemble Enable voting ensemble run. bool
ensembleModelDownloadTimeout During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded.
Configure this parameter with a higher value than 300 secs, if more time is needed.		 string
stackEnsembleSettings Stack ensemble settings for stack ensemble run. StackEnsembleSettings
trainingMode TrainingMode mode - Setting to 'auto' is same as setting it to 'non-distributed' for now, however in the future may result in mixed mode or heuristics based mode selection. Default is 'auto'.
If 'Distributed' then only distributed featurization is used and distributed algorithms are chosen.
If 'NonDistributed' then only non distributed algorithms are chosen.
 'Auto'
'Distributed'
'NonDistributed'

ImageClassification

Name Description Value
taskType [Required] Task type for AutoMLJob. 'ImageClassification' (required)
limitSettings [Required] Limit settings for the AutoML job. ImageLimitSettings (required)
modelSettings Settings used for training the model. ImageModelSettingsClassification
primaryMetric Primary metric to optimize for this task. 'AUCWeighted'
'Accuracy'
'AveragePrecisionScoreWeighted'
'NormMacroRecall'
'PrecisionScoreWeighted'
searchSpace Search space for sampling different combinations of models and their hyperparameters. ImageModelDistributionSettingsClassification[]
sweepSettings Model sweeping and hyperparameter sweeping related settings. ImageSweepSettings
validationData Validation data inputs. MLTableJobInput
validationDataSize The fraction of training dataset that needs to be set aside for validation purpose.
Values between (0.0 , 1.0)
Applied when validation dataset is not provided.
 int

ImageLimitSettings

Name Description Value
maxConcurrentTrials Maximum number of concurrent AutoML iterations. int
maxTrials Maximum number of AutoML iterations. int
timeout AutoML job timeout. string

ImageModelSettingsClassification

Name Description Value
advancedSettings Settings for advanced scenarios. string
amsGradient Enable AMSGrad when optimizer is 'adam' or 'adamw'. bool
augmentations Settings for using Augmentations. string
beta1 Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. int
beta2 Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. int
checkpointFrequency Frequency to store model checkpoints. Must be a positive integer. int
checkpointModel The pretrained checkpoint model for incremental training. MLFlowModelJobInput
checkpointRunId The id of a previous run that has a pretrained checkpoint for incremental training. string
distributed Whether to use distributed training. bool
earlyStopping Enable early stopping logic during training. bool
earlyStoppingDelay Minimum number of epochs or validation evaluations to wait before primary metric improvement
is tracked for early stopping. Must be a positive integer.		 int
earlyStoppingPatience Minimum number of epochs or validation evaluations with no primary metric improvement before
the run is stopped. Must be a positive integer.		 int
enableOnnxNormalization Enable normalization when exporting ONNX model. bool
evaluationFrequency Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. int
gradientAccumulationStep Gradient accumulation means running a configured number of "GradAccumulationStep" steps without
updating the model weights while accumulating the gradients of those steps, and then using
the accumulated gradients to compute the weight updates. Must be a positive integer.		 int
layersToFreeze Number of layers to freeze for the model. Must be a positive integer.
For instance, passing 2 as value for 'seresnext' means
freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please
see: /azure/machine-learning/how-to-auto-train-image-models.		 int
learningRate Initial learning rate. Must be a float in the range [0, 1]. int
learningRateScheduler Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. 'None'
'Step'
'WarmupCosine'
modelName Name of the model to use for training.
For more information on the available models please visit the official documentation:
/azure/machine-learning/how-to-auto-train-image-models.		 string
momentum Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. int
nesterov Enable nesterov when optimizer is 'sgd'. bool
numberOfEpochs Number of training epochs. Must be a positive integer. int
numberOfWorkers Number of data loader workers. Must be a non-negative integer. int
optimizer Type of optimizer. 'Adam'
'Adamw'
'None'
'Sgd'
randomSeed Random seed to be used when using deterministic training. int
stepLRGamma Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. int
stepLRStepSize Value of step size when learning rate scheduler is 'step'. Must be a positive integer. int
trainingBatchSize Training batch size. Must be a positive integer. int
trainingCropSize Image crop size that is input to the neural network for the training dataset. Must be a positive integer. int
validationBatchSize Validation batch size. Must be a positive integer. int
validationCropSize Image crop size that is input to the neural network for the validation dataset. Must be a positive integer. int
validationResizeSize Image size to which to resize before cropping for validation dataset. Must be a positive integer. int
warmupCosineLRCycles Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. int
warmupCosineLRWarmupEpochs Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. int
weightDecay Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. int
weightedLoss Weighted loss. The accepted values are 0 for no weighted loss.
1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.
 int

MLFlowModelJobInput

Name Description Value
description Description for the input. string
jobInputType [Required] Specifies the type of job. 'custom_model'
'literal'
'mlflow_model'
'mltable'
'triton_model'
'uri_file'
'uri_folder' (required)
mode Input Asset Delivery Mode. 'Direct'
'Download'
'EvalDownload'
'EvalMount'
'ReadOnlyMount'
'ReadWriteMount'
uri [Required] Input Asset URI. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]

ImageModelDistributionSettingsClassification

Name Description Value
amsGradient Enable AMSGrad when optimizer is 'adam' or 'adamw'. string
augmentations Settings for using Augmentations. string
beta1 Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. string
beta2 Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. string
distributed Whether to use distributer training. string
earlyStopping Enable early stopping logic during training. string
earlyStoppingDelay Minimum number of epochs or validation evaluations to wait before primary metric improvement
is tracked for early stopping. Must be a positive integer.		 string
earlyStoppingPatience Minimum number of epochs or validation evaluations with no primary metric improvement before
the run is stopped. Must be a positive integer.		 string
enableOnnxNormalization Enable normalization when exporting ONNX model. string
evaluationFrequency Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. string
gradientAccumulationStep Gradient accumulation means running a configured number of "GradAccumulationStep" steps without
updating the model weights while accumulating the gradients of those steps, and then using
the accumulated gradients to compute the weight updates. Must be a positive integer.		 string
layersToFreeze Number of layers to freeze for the model. Must be a positive integer.
For instance, passing 2 as value for 'seresnext' means
freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please
see: /azure/machine-learning/how-to-auto-train-image-models.		 string
learningRate Initial learning rate. Must be a float in the range [0, 1]. string
learningRateScheduler Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. string
modelName Name of the model to use for training.
For more information on the available models please visit the official documentation:
/azure/machine-learning/how-to-auto-train-image-models.		 string
momentum Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. string
nesterov Enable nesterov when optimizer is 'sgd'. string
numberOfEpochs Number of training epochs. Must be a positive integer. string
numberOfWorkers Number of data loader workers. Must be a non-negative integer. string
optimizer Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'. string
randomSeed Random seed to be used when using deterministic training. string
stepLRGamma Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. string
stepLRStepSize Value of step size when learning rate scheduler is 'step'. Must be a positive integer. string
trainingBatchSize Training batch size. Must be a positive integer. string
trainingCropSize Image crop size that is input to the neural network for the training dataset. Must be a positive integer. string
validationBatchSize Validation batch size. Must be a positive integer. string
validationCropSize Image crop size that is input to the neural network for the validation dataset. Must be a positive integer. string
validationResizeSize Image size to which to resize before cropping for validation dataset. Must be a positive integer. string
warmupCosineLRCycles Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. string
warmupCosineLRWarmupEpochs Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. string
weightDecay Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. string
weightedLoss Weighted loss. The accepted values are 0 for no weighted loss.
1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.
 string

ImageSweepSettings

Name Description Value
earlyTermination Type of early termination policy. EarlyTerminationPolicy
samplingAlgorithm [Required] Type of the hyperparameter sampling algorithms. 'Bayesian'
'Grid'
'Random' (required)

ImageClassificationMultilabel

Name Description Value
taskType [Required] Task type for AutoMLJob. 'ImageClassificationMultilabel' (required)
limitSettings [Required] Limit settings for the AutoML job. ImageLimitSettings (required)
modelSettings Settings used for training the model. ImageModelSettingsClassification
primaryMetric Primary metric to optimize for this task. 'AUCWeighted'
'Accuracy'
'AveragePrecisionScoreWeighted'
'IOU'
'NormMacroRecall'
'PrecisionScoreWeighted'
searchSpace Search space for sampling different combinations of models and their hyperparameters. ImageModelDistributionSettingsClassification[]
sweepSettings Model sweeping and hyperparameter sweeping related settings. ImageSweepSettings
validationData Validation data inputs. MLTableJobInput
validationDataSize The fraction of training dataset that needs to be set aside for validation purpose.
Values between (0.0 , 1.0)
Applied when validation dataset is not provided.
 int

ImageInstanceSegmentation

Name Description Value
taskType [Required] Task type for AutoMLJob. 'ImageInstanceSegmentation' (required)
limitSettings [Required] Limit settings for the AutoML job. ImageLimitSettings (required)
modelSettings Settings used for training the model. ImageModelSettingsObjectDetection
primaryMetric Primary metric to optimize for this task. 'MeanAveragePrecision'
searchSpace Search space for sampling different combinations of models and their hyperparameters. ImageModelDistributionSettingsObjectDetection[]
sweepSettings Model sweeping and hyperparameter sweeping related settings. ImageSweepSettings
validationData Validation data inputs. MLTableJobInput
validationDataSize The fraction of training dataset that needs to be set aside for validation purpose.
Values between (0.0 , 1.0)
Applied when validation dataset is not provided.
 int

ImageModelSettingsObjectDetection

Name Description Value
advancedSettings Settings for advanced scenarios. string
amsGradient Enable AMSGrad when optimizer is 'adam' or 'adamw'. bool
augmentations Settings for using Augmentations. string
beta1 Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. int
beta2 Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. int
boxDetectionsPerImage Maximum number of detections per image, for all classes. Must be a positive integer.
Note: This settings is not supported for the 'yolov5' algorithm.		 int
boxScoreThreshold During inference, only return proposals with a classification score greater than
BoxScoreThreshold. Must be a float in the range[0, 1].		 int
checkpointFrequency Frequency to store model checkpoints. Must be a positive integer. int
checkpointModel The pretrained checkpoint model for incremental training. MLFlowModelJobInput
checkpointRunId The id of a previous run that has a pretrained checkpoint for incremental training. string
distributed Whether to use distributed training. bool
earlyStopping Enable early stopping logic during training. bool
earlyStoppingDelay Minimum number of epochs or validation evaluations to wait before primary metric improvement
is tracked for early stopping. Must be a positive integer.		 int
earlyStoppingPatience Minimum number of epochs or validation evaluations with no primary metric improvement before
the run is stopped. Must be a positive integer.		 int
enableOnnxNormalization Enable normalization when exporting ONNX model. bool
evaluationFrequency Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. int
gradientAccumulationStep Gradient accumulation means running a configured number of "GradAccumulationStep" steps without
updating the model weights while accumulating the gradients of those steps, and then using
the accumulated gradients to compute the weight updates. Must be a positive integer.		 int
imageSize Image size for train and validation. Must be a positive integer.
Note: The training run may get into CUDA OOM if the size is too big.
Note: This settings is only supported for the 'yolov5' algorithm.		 int
layersToFreeze Number of layers to freeze for the model. Must be a positive integer.
For instance, passing 2 as value for 'seresnext' means
freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please
see: /azure/machine-learning/how-to-auto-train-image-models.		 int
learningRate Initial learning rate. Must be a float in the range [0, 1]. int
learningRateScheduler Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. 'None'
'Step'
'WarmupCosine'
logTrainingMetrics Enable computing and logging training metrics. 'Disable'
'Enable'
logValidationLoss Enable computing and logging validation loss. 'Disable'
'Enable'
maxSize Maximum size of the image to be rescaled before feeding it to the backbone.
Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
Note: This settings is not supported for the 'yolov5' algorithm.		 int
minSize Minimum size of the image to be rescaled before feeding it to the backbone.
Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
Note: This settings is not supported for the 'yolov5' algorithm.		 int
modelName Name of the model to use for training.
For more information on the available models please visit the official documentation:
/azure/machine-learning/how-to-auto-train-image-models.		 string
modelSize Model size. Must be 'small', 'medium', 'large', or 'xlarge'.
Note: training run may get into CUDA OOM if the model size is too big.
Note: This settings is only supported for the 'yolov5' algorithm.		 'ExtraLarge'
'Large'
'Medium'
'None'
'Small'
momentum Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. int
multiScale Enable multi-scale image by varying image size by +/- 50%.
Note: training run may get into CUDA OOM if no sufficient GPU memory.
Note: This settings is only supported for the 'yolov5' algorithm.		 bool
nesterov Enable nesterov when optimizer is 'sgd'. bool
nmsIouThreshold IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1]. int
numberOfEpochs Number of training epochs. Must be a positive integer. int
numberOfWorkers Number of data loader workers. Must be a non-negative integer. int
optimizer Type of optimizer. 'Adam'
'Adamw'
'None'
'Sgd'
randomSeed Random seed to be used when using deterministic training. int
stepLRGamma Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. int
stepLRStepSize Value of step size when learning rate scheduler is 'step'. Must be a positive integer. int
tileGridSize The grid size to use for tiling each image. Note: TileGridSize must not be
None to enable small object detection logic. A string containing two integers in mxn format.
Note: This settings is not supported for the 'yolov5' algorithm.		 string
tileOverlapRatio Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1).
Note: This settings is not supported for the 'yolov5' algorithm.		 int
tilePredictionsNmsThreshold The IOU threshold to use to perform NMS while merging predictions from tiles and image.
Used in validation/ inference. Must be float in the range [0, 1].
Note: This settings is not supported for the 'yolov5' algorithm.		 int
trainingBatchSize Training batch size. Must be a positive integer. int
validationBatchSize Validation batch size. Must be a positive integer. int
validationIouThreshold IOU threshold to use when computing validation metric. Must be float in the range [0, 1]. int
validationMetricType Metric computation method to use for validation metrics. 'Coco'
'CocoVoc'
'None'
'Voc'
warmupCosineLRCycles Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. int
warmupCosineLRWarmupEpochs Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. int
weightDecay Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. int

ImageModelDistributionSettingsObjectDetection

Name Description Value
amsGradient Enable AMSGrad when optimizer is 'adam' or 'adamw'. string
augmentations Settings for using Augmentations. string
beta1 Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. string
beta2 Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. string
boxDetectionsPerImage Maximum number of detections per image, for all classes. Must be a positive integer.
Note: This settings is not supported for the 'yolov5' algorithm.		 string
boxScoreThreshold During inference, only return proposals with a classification score greater than
BoxScoreThreshold. Must be a float in the range[0, 1].		 string
distributed Whether to use distributer training. string
earlyStopping Enable early stopping logic during training. string
earlyStoppingDelay Minimum number of epochs or validation evaluations to wait before primary metric improvement
is tracked for early stopping. Must be a positive integer.		 string
earlyStoppingPatience Minimum number of epochs or validation evaluations with no primary metric improvement before
the run is stopped. Must be a positive integer.		 string
enableOnnxNormalization Enable normalization when exporting ONNX model. string
evaluationFrequency Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. string
gradientAccumulationStep Gradient accumulation means running a configured number of "GradAccumulationStep" steps without
updating the model weights while accumulating the gradients of those steps, and then using
the accumulated gradients to compute the weight updates. Must be a positive integer.		 string
imageSize Image size for train and validation. Must be a positive integer.
Note: The training run may get into CUDA OOM if the size is too big.
Note: This settings is only supported for the 'yolov5' algorithm.		 string
layersToFreeze Number of layers to freeze for the model. Must be a positive integer.
For instance, passing 2 as value for 'seresnext' means
freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please
see: /azure/machine-learning/how-to-auto-train-image-models.		 string
learningRate Initial learning rate. Must be a float in the range [0, 1]. string
learningRateScheduler Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. string
maxSize Maximum size of the image to be rescaled before feeding it to the backbone.
Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
Note: This settings is not supported for the 'yolov5' algorithm.		 string
minSize Minimum size of the image to be rescaled before feeding it to the backbone.
Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
Note: This settings is not supported for the 'yolov5' algorithm.		 string
modelName Name of the model to use for training.
For more information on the available models please visit the official documentation:
/azure/machine-learning/how-to-auto-train-image-models.		 string
modelSize Model size. Must be 'small', 'medium', 'large', or 'xlarge'.
Note: training run may get into CUDA OOM if the model size is too big.
Note: This settings is only supported for the 'yolov5' algorithm.		 string
momentum Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. string
multiScale Enable multi-scale image by varying image size by +/- 50%.
Note: training run may get into CUDA OOM if no sufficient GPU memory.
Note: This settings is only supported for the 'yolov5' algorithm.		 string
nesterov Enable nesterov when optimizer is 'sgd'. string
nmsIouThreshold IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1]. string
numberOfEpochs Number of training epochs. Must be a positive integer. string
numberOfWorkers Number of data loader workers. Must be a non-negative integer. string
optimizer Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'. string
randomSeed Random seed to be used when using deterministic training. string
stepLRGamma Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. string
stepLRStepSize Value of step size when learning rate scheduler is 'step'. Must be a positive integer. string
tileGridSize The grid size to use for tiling each image. Note: TileGridSize must not be
None to enable small object detection logic. A string containing two integers in mxn format.
Note: This settings is not supported for the 'yolov5' algorithm.		 string
tileOverlapRatio Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1).
Note: This settings is not supported for the 'yolov5' algorithm.		 string
tilePredictionsNmsThreshold The IOU threshold to use to perform NMS while merging predictions from tiles and image.
Used in validation/ inference. Must be float in the range [0, 1].
Note: This settings is not supported for the 'yolov5' algorithm.
NMS: Non-maximum suppression		 string
trainingBatchSize Training batch size. Must be a positive integer. string
validationBatchSize Validation batch size. Must be a positive integer. string
validationIouThreshold IOU threshold to use when computing validation metric. Must be float in the range [0, 1]. string
validationMetricType Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'. string
warmupCosineLRCycles Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. string
warmupCosineLRWarmupEpochs Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. string
weightDecay Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. string

ImageObjectDetection

Name Description Value
taskType [Required] Task type for AutoMLJob. 'ImageObjectDetection' (required)
limitSettings [Required] Limit settings for the AutoML job. ImageLimitSettings (required)
modelSettings Settings used for training the model. ImageModelSettingsObjectDetection
primaryMetric Primary metric to optimize for this task. 'MeanAveragePrecision'
searchSpace Search space for sampling different combinations of models and their hyperparameters. ImageModelDistributionSettingsObjectDetection[]
sweepSettings Model sweeping and hyperparameter sweeping related settings. ImageSweepSettings
validationData Validation data inputs. MLTableJobInput
validationDataSize The fraction of training dataset that needs to be set aside for validation purpose.
Values between (0.0 , 1.0)
Applied when validation dataset is not provided.
 int

Regression

Name Description Value
taskType [Required] Task type for AutoMLJob. 'Regression' (required)
cvSplitColumnNames Columns to use for CVSplit data. string[]
featurizationSettings Featurization inputs needed for AutoML job. TableVerticalFeaturizationSettings
fixedParameters Model/training parameters that will remain constant throughout training. TableFixedParameters
limitSettings Execution constraints for AutoMLJob. TableVerticalLimitSettings
nCrossValidations Number of cross validation folds to be applied on training dataset
when validation dataset is not provided.		 NCrossValidations
primaryMetric Primary metric for regression task. 'NormalizedMeanAbsoluteError'
'NormalizedRootMeanSquaredError'
'R2Score'
'SpearmanCorrelation'
searchSpace Search space for sampling different combinations of models and their hyperparameters. TableParameterSubspace[]
sweepSettings Settings for model sweeping and hyperparameter tuning. TableSweepSettings
testData Test data input. MLTableJobInput
testDataSize The fraction of test dataset that needs to be set aside for validation purpose.
Values between (0.0 , 1.0)
Applied when validation dataset is not provided.		 int
trainingSettings Inputs for training phase for an AutoML Job. RegressionTrainingSettings
validationData Validation data inputs. MLTableJobInput
validationDataSize The fraction of training dataset that needs to be set aside for validation purpose.
Values between (0.0 , 1.0)
Applied when validation dataset is not provided.		 int
weightColumnName The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down. string

RegressionTrainingSettings

Name Description Value
allowedTrainingAlgorithms Allowed models for regression task. String array containing any of:
'DecisionTree'
'ElasticNet'
'ExtremeRandomTrees'
'GradientBoosting'
'KNN'
'LassoLars'
'LightGBM'
'RandomForest'
'SGD'
'XGBoostRegressor'
blockedTrainingAlgorithms Blocked models for regression task. String array containing any of:
'DecisionTree'
'ElasticNet'
'ExtremeRandomTrees'
'GradientBoosting'
'KNN'
'LassoLars'
'LightGBM'
'RandomForest'
'SGD'
'XGBoostRegressor'
enableDnnTraining Enable recommendation of DNN models. bool
enableModelExplainability Flag to turn on explainability on best model. bool
enableOnnxCompatibleModels Flag for enabling onnx compatible models. bool
enableStackEnsemble Enable stack ensemble run. bool
enableVoteEnsemble Enable voting ensemble run. bool
ensembleModelDownloadTimeout During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded.
Configure this parameter with a higher value than 300 secs, if more time is needed.		 string
stackEnsembleSettings Stack ensemble settings for stack ensemble run. StackEnsembleSettings
trainingMode TrainingMode mode - Setting to 'auto' is same as setting it to 'non-distributed' for now, however in the future may result in mixed mode or heuristics based mode selection. Default is 'auto'.
If 'Distributed' then only distributed featurization is used and distributed algorithms are chosen.
If 'NonDistributed' then only non distributed algorithms are chosen.
 'Auto'
'Distributed'
'NonDistributed'

TextClassification

Name Description Value
taskType [Required] Task type for AutoMLJob. 'TextClassification' (required)
featurizationSettings Featurization inputs needed for AutoML job. NlpVerticalFeaturizationSettings
fixedParameters Model/training parameters that will remain constant throughout training. NlpFixedParameters
limitSettings Execution constraints for AutoMLJob. NlpVerticalLimitSettings
primaryMetric Primary metric for Text-Classification task. 'AUCWeighted'
'Accuracy'
'AveragePrecisionScoreWeighted'
'NormMacroRecall'
'PrecisionScoreWeighted'
searchSpace Search space for sampling different combinations of models and their hyperparameters. NlpParameterSubspace[]
sweepSettings Settings for model sweeping and hyperparameter tuning. NlpSweepSettings
validationData Validation data inputs. MLTableJobInput

NlpVerticalFeaturizationSettings

Name Description Value
datasetLanguage Dataset language, useful for the text data. string

NlpFixedParameters

Name Description Value
gradientAccumulationSteps Number of steps to accumulate gradients over before running a backward pass. int
learningRate The learning rate for the training procedure. int
learningRateScheduler The type of learning rate schedule to use during the training procedure. 'Constant'
'ConstantWithWarmup'
'Cosine'
'CosineWithRestarts'
'Linear'
'None'
'Polynomial'
modelName The name of the model to train. string
numberOfEpochs Number of training epochs. int
trainingBatchSize The batch size for the training procedure. int
validationBatchSize The batch size to be used during evaluation. int
warmupRatio The warmup ratio, used alongside LrSchedulerType. int
weightDecay The weight decay for the training procedure. int

NlpVerticalLimitSettings

Name Description Value
maxConcurrentTrials Maximum Concurrent AutoML iterations. int
maxNodes Maximum nodes to use for the experiment. int
maxTrials Number of AutoML iterations. int
timeout AutoML job timeout. string
trialTimeout Timeout for individual HD trials. string

NlpParameterSubspace

Name Description Value
gradientAccumulationSteps Number of steps to accumulate gradients over before running a backward pass. string
learningRate The learning rate for the training procedure. string
learningRateScheduler The type of learning rate schedule to use during the training procedure. string
modelName The name of the model to train. string
numberOfEpochs Number of training epochs. string
trainingBatchSize The batch size for the training procedure. string
validationBatchSize The batch size to be used during evaluation. string
warmupRatio The warmup ratio, used alongside LrSchedulerType. string
weightDecay The weight decay for the training procedure. string

NlpSweepSettings

Name Description Value
earlyTermination Type of early termination policy for the sweeping job. EarlyTerminationPolicy
samplingAlgorithm [Required] Type of sampling algorithm. 'Bayesian'
'Grid'
'Random' (required)

TextClassificationMultilabel

Name Description Value
taskType [Required] Task type for AutoMLJob. 'TextClassificationMultilabel' (required)
featurizationSettings Featurization inputs needed for AutoML job. NlpVerticalFeaturizationSettings
fixedParameters Model/training parameters that will remain constant throughout training. NlpFixedParameters
limitSettings Execution constraints for AutoMLJob. NlpVerticalLimitSettings
searchSpace Search space for sampling different combinations of models and their hyperparameters. NlpParameterSubspace[]
sweepSettings Settings for model sweeping and hyperparameter tuning. NlpSweepSettings
validationData Validation data inputs. MLTableJobInput

TextNer

Name Description Value
taskType [Required] Task type for AutoMLJob. 'TextNER' (required)
featurizationSettings Featurization inputs needed for AutoML job. NlpVerticalFeaturizationSettings
fixedParameters Model/training parameters that will remain constant throughout training. NlpFixedParameters
limitSettings Execution constraints for AutoMLJob. NlpVerticalLimitSettings
searchSpace Search space for sampling different combinations of models and their hyperparameters. NlpParameterSubspace[]
sweepSettings Settings for model sweeping and hyperparameter tuning. NlpSweepSettings
validationData Validation data inputs. MLTableJobInput

CommandJob

Name Description Value
jobType [Required] Specifies the type of job. 'Command' (required)
autologgerSettings Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null. AutologgerSettings
codeId ARM resource ID of the code asset. string
command [Required] The command to execute on startup of the job. eg. "python train.py" string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]
distribution Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, Ray, or null. DistributionConfiguration
environmentId [Required] The ARM resource ID of the Environment specification for the job. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]
environmentVariables Environment variables included in the job. CommandJobEnvironmentVariables
inputs Mapping of input data bindings used in the job. CommandJobInputs
limits Command Job limit. CommandJobLimits
outputs Mapping of output data bindings used in the job. CommandJobOutputs
queueSettings Queue settings for the job QueueSettings
resources Compute Resource configuration for the job. JobResourceConfiguration

AutologgerSettings

Name Description Value
mlflowAutologger [Required] Indicates whether mlflow autologger is enabled. 'Disabled'
'Enabled' (required)

DistributionConfiguration

Name Description Value
distributionType Set the object type Mpi
PyTorch
Ray
TensorFlow (required)

Mpi

Name Description Value
distributionType [Required] Specifies the type of distribution framework. 'Mpi' (required)
processCountPerInstance Number of processes per MPI node. int

PyTorch

Name Description Value
distributionType [Required] Specifies the type of distribution framework. 'PyTorch' (required)
processCountPerInstance Number of processes per node. int

Ray

Name Description Value
distributionType [Required] Specifies the type of distribution framework. 'Ray' (required)
address The address of Ray head node. string
dashboardPort The port to bind the dashboard server to. int
headNodeAdditionalArgs Additional arguments passed to ray start in head node. string
includeDashboard Provide this argument to start the Ray dashboard GUI. bool
port The port of the head ray process. int
workerNodeAdditionalArgs Additional arguments passed to ray start in worker node. string

TensorFlow

Name Description Value
distributionType [Required] Specifies the type of distribution framework. 'TensorFlow' (required)
parameterServerCount Number of parameter server tasks. int
workerCount Number of workers. If not specified, will default to the instance count. int

CommandJobEnvironmentVariables

Name Description Value
{customized property} string

CommandJobInputs

Name Description Value
{customized property} JobInput

JobInput

Name Description Value
description Description for the input. string
jobInputType Set the object type custom_model
literal
mlflow_model
mltable
triton_model
uri_file
uri_folder (required)

CustomModelJobInput

Name Description Value
jobInputType [Required] Specifies the type of job. 'custom_model' (required)
mode Input Asset Delivery Mode. 'Direct'
'Download'
'EvalDownload'
'EvalMount'
'ReadOnlyMount'
'ReadWriteMount'
uri [Required] Input Asset URI. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]

LiteralJobInput

Name Description Value
jobInputType [Required] Specifies the type of job. 'literal' (required)
value [Required] Literal value for the input. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]

TritonModelJobInput

Name Description Value
jobInputType [Required] Specifies the type of job. 'triton_model' (required)
mode Input Asset Delivery Mode. 'Direct'
'Download'
'EvalDownload'
'EvalMount'
'ReadOnlyMount'
'ReadWriteMount'
uri [Required] Input Asset URI. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]

UriFileJobInput

Name Description Value
jobInputType [Required] Specifies the type of job. 'uri_file' (required)
mode Input Asset Delivery Mode. 'Direct'
'Download'
'EvalDownload'
'EvalMount'
'ReadOnlyMount'
'ReadWriteMount'
uri [Required] Input Asset URI. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]

UriFolderJobInput

Name Description Value
jobInputType [Required] Specifies the type of job. 'uri_folder' (required)
mode Input Asset Delivery Mode. 'Direct'
'Download'
'EvalDownload'
'EvalMount'
'ReadOnlyMount'
'ReadWriteMount'
uri [Required] Input Asset URI. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]

CommandJobLimits

Name Description Value
jobLimitsType [Required] JobLimit type. 'Command'
'Sweep' (required)
timeout The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds. string

CommandJobOutputs

Name Description Value
{customized property} JobOutput

LabelingJobProperties

Name Description Value
componentId ARM resource ID of the component resource. string
computeId ARM resource ID of the compute resource. string
dataConfiguration Configuration of data used in the job. LabelingDataConfiguration
description The asset description text. string
displayName Display name of job. string
experimentName The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment. string
identity Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null.
Defaults to AmlToken if null.		 IdentityConfiguration
isArchived Is the asset archived? bool
jobInstructions Labeling instructions of the job. LabelingJobInstructions
jobType [Required] Specifies the type of job. 'AutoML'
'Command'
'Labeling'
'Pipeline'
'Spark'
'Sweep' (required)
labelCategories Label categories of the job. LabelingJobLabelCategories
labelingJobMediaProperties Media type specific properties in the job. LabelingJobMediaProperties
mlAssistConfiguration Configuration of MLAssist feature in the job. MLAssistConfiguration
notificationSetting Notification setting for the job NotificationSetting
properties The asset property dictionary. ResourceBaseProperties
secretsConfiguration Configuration for secrets to be made available during runtime. JobBaseSecretsConfiguration
services List of JobEndpoints.
For local jobs, a job endpoint will have an endpoint value of FileStreamObject.		 JobBaseServices
tags Tag dictionary. Tags can be added, removed, and updated. object

LabelingDataConfiguration

Name Description Value
dataId Resource Id of the data asset to perform labeling. string
incrementalDataRefresh Indicates whether to enable incremental data refresh. 'Disabled'
'Enabled'

LabelingJobInstructions

Name Description Value
uri The link to a page with detailed labeling instructions for labelers. string

LabelingJobLabelCategories

Name Description Value
{customized property} LabelCategory

LabelCategory

Name Description Value
classes Dictionary of label classes in this category. LabelCategoryClasses
displayName Display name of the label category. string
multiSelect Indicates whether it is allowed to select multiple classes in this category. 'Disabled'
'Enabled'

LabelCategoryClasses

Name Description Value
{customized property} LabelClass

LabelClass

Name Description Value
displayName Display name of the label class. string
subclasses Dictionary of subclasses of the label class. LabelClassSubclasses

LabelClassSubclasses

Name Description Value
{customized property} LabelClass

LabelingJobMediaProperties

Name Description Value
mediaType Set the object type Image
Text (required)

LabelingJobImageProperties

Name Description Value
mediaType [Required] Media type of the job. 'Image' (required)
annotationType Annotation type of image labeling job. 'BoundingBox'
'Classification'
'InstanceSegmentation'

LabelingJobTextProperties

Name Description Value
mediaType [Required] Media type of the job. 'Text' (required)
annotationType Annotation type of text labeling job. 'Classification'
'NamedEntityRecognition'

MLAssistConfiguration

Name Description Value
mlAssist Set the object type Disabled
Enabled (required)

MLAssistConfigurationDisabled

Name Description Value
mlAssist [Required] Indicates whether MLAssist feature is enabled. 'Disabled' (required)

MLAssistConfigurationEnabled

Name Description Value
mlAssist [Required] Indicates whether MLAssist feature is enabled. 'Enabled' (required)
inferencingComputeBinding [Required] AML compute binding used in inferencing. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]
trainingComputeBinding [Required] AML compute binding used in training. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]

PipelineJob

Name Description Value
jobType [Required] Specifies the type of job. 'Pipeline' (required)
inputs Inputs for the pipeline job. PipelineJobInputs
jobs Jobs construct the Pipeline Job. PipelineJobJobs
outputs Outputs for the pipeline job PipelineJobOutputs
settings Pipeline settings, for things like ContinueRunOnStepFailure etc.
sourceJobId ARM resource ID of source job. string

PipelineJobInputs

Name Description Value
{customized property} JobInput

PipelineJobJobs

Name Description Value
{customized property}

PipelineJobOutputs

Name Description Value
{customized property} JobOutput

SparkJob

Name Description Value
jobType [Required] Specifies the type of job. 'Spark' (required)
archives Archive files used in the job. string[]
args Arguments for the job. string
codeId [Required] ARM resource ID of the code asset. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]
conf Spark configured properties. SparkJobConf
entry [Required] The entry to execute on startup of the job. SparkJobEntry (required)
environmentId The ARM resource ID of the Environment specification for the job. string
files Files used in the job. string[]
inputs Mapping of input data bindings used in the job. SparkJobInputs
jars Jar files used in the job. string[]
outputs Mapping of output data bindings used in the job. SparkJobOutputs
pyFiles Python files used in the job. string[]
queueSettings Queue settings for the job QueueSettings
resources Compute Resource configuration for the job. SparkResourceConfiguration

SparkJobConf

Name Description Value
{customized property} string

SparkJobEntry

Name Description Value
sparkJobEntryType Set the object type SparkJobPythonEntry
SparkJobScalaEntry (required)

SparkJobPythonEntry

Name Description Value
sparkJobEntryType [Required] Type of the job's entry point. 'SparkJobPythonEntry' (required)
file [Required] Relative python file path for job entry point. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]

SparkJobScalaEntry

Name Description Value
sparkJobEntryType [Required] Type of the job's entry point. 'SparkJobScalaEntry' (required)
className [Required] Scala class name used as entry point. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]

SparkJobInputs

Name Description Value
{customized property} JobInput

SparkJobOutputs

Name Description Value
{customized property} JobOutput

SparkResourceConfiguration

Name Description Value
instanceType Optional type of VM used as supported by the compute target. string
runtimeVersion Version of spark runtime used for the job. string

SweepJob

Name Description Value
jobType [Required] Specifies the type of job. 'Sweep' (required)
earlyTermination Early termination policies enable canceling poor-performing runs before they complete EarlyTerminationPolicy
inputs Mapping of input data bindings used in the job. SweepJobInputs
limits Sweep Job limit. SweepJobLimits
objective [Required] Optimization objective. Objective (required)
outputs Mapping of output data bindings used in the job. SweepJobOutputs
queueSettings Queue settings for the job QueueSettings
samplingAlgorithm [Required] The hyperparameter sampling algorithm SamplingAlgorithm (required)
searchSpace [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
trial [Required] Trial component definition. TrialComponent (required)

SweepJobInputs

Name Description Value
{customized property} JobInput

SweepJobLimits

Name Description Value
jobLimitsType [Required] JobLimit type. 'Command'
'Sweep' (required)
maxConcurrentTrials Sweep Job max concurrent trials. int
maxTotalTrials Sweep Job max total trials. int
timeout The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds. string
trialTimeout Sweep Job Trial timeout value. string

Objective

Name Description Value
goal [Required] Defines supported metric goals for hyperparameter tuning 'Maximize'
'Minimize' (required)
primaryMetric [Required] Name of the metric to optimize. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]

SweepJobOutputs

Name Description Value
{customized property} JobOutput

SamplingAlgorithm

Name Description Value
samplingAlgorithmType Set the object type Bayesian
Grid
Random (required)

BayesianSamplingAlgorithm

Name Description Value
samplingAlgorithmType [Required] The algorithm used for generating hyperparameter values, along with configuration properties 'Bayesian' (required)

GridSamplingAlgorithm

Name Description Value
samplingAlgorithmType [Required] The algorithm used for generating hyperparameter values, along with configuration properties 'Grid' (required)

RandomSamplingAlgorithm

Name Description Value
samplingAlgorithmType [Required] The algorithm used for generating hyperparameter values, along with configuration properties 'Random' (required)
logbase An optional positive number or e in string format to be used as base for log based random sampling string
rule The specific type of random algorithm 'Random'
'Sobol'
seed An optional integer to use as the seed for random number generation int

TrialComponent

Name Description Value
codeId ARM resource ID of the code asset. string
command [Required] The command to execute on startup of the job. eg. "python train.py" string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]
distribution Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null. DistributionConfiguration
environmentId [Required] The ARM resource ID of the Environment specification for the job. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]
environmentVariables Environment variables included in the job. TrialComponentEnvironmentVariables
resources Compute Resource configuration for the job. JobResourceConfiguration

TrialComponentEnvironmentVariables

Name Description Value
{customized property} string

CreateMonitorAction

Name Description Value
actionType [Required] Specifies the action type of the schedule 'CreateMonitor' (required)
monitorDefinition [Required] Defines the monitor. MonitorDefinition (required)

MonitorDefinition

Name Description Value
alertNotificationSetting The monitor's notification settings. MonitoringAlertNotificationSettingsBase
computeId [Required] The ARM resource ID of the compute resource to run the monitoring job on. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]
monitoringTarget The ARM resource ID of either the model or deployment targeted by this monitor. string
signals [Required] The signals to monitor. MonitorDefinitionSignals (required)

MonitoringAlertNotificationSettingsBase

Name Description Value
alertNotificationType Set the object type AzureMonitor
Email (required)

AzMonMonitoringAlertNotificationSettings

Name Description Value
alertNotificationType [Required] Specifies the type of signal to monitor. 'AzureMonitor' (required)

EmailMonitoringAlertNotificationSettings

Name Description Value
alertNotificationType [Required] Specifies the type of signal to monitor. 'Email' (required)
emailNotificationSetting Configuration for notification. NotificationSetting

MonitorDefinitionSignals

Name Description Value
{customized property} MonitoringSignalBase

MonitoringSignalBase

Name Description Value
lookbackPeriod The amount of time a single monitor should look back over the target data on a given run. string
mode The current notification mode for this signal. 'Disabled'
'Enabled'
signalType Set the object type Custom
DataDrift
DataQuality
FeatureAttributionDrift
ModelPerformance
PredictionDrift (required)

CustomMonitoringSignal

Name Description Value
signalType [Required] Specifies the type of signal to monitor. 'Custom' (required)
componentId [Required] ARM resource ID of the component resource used to calculate the custom metrics. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]
inputAssets Monitoring assets to take as input. Key is the component input port name, value is the data asset. CustomMonitoringSignalInputAssets
metricThresholds [Required] A list of metrics to calculate and their associated thresholds. CustomMetricThreshold[] (required)

CustomMonitoringSignalInputAssets

Name Description Value
{customized property} MonitoringInputData

MonitoringInputData

Name Description Value
asset The data asset input to be leveraged by the monitoring job..
dataContext [Required] The context of the data source. 'GroundTruth'
'ModelInputs'
'ModelOutputs'
'Test'
'Training'
'Validation' (required)
preprocessingComponentId The ARM resource ID of the component resource used to preprocess the data. string
targetColumnName The target column in the given data asset to leverage. string

CustomMetricThreshold

Name Description Value
metric [Required] The user-defined metric to calculate. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]
threshold The threshold value. If null, a default value will be set depending on the selected metric. MonitoringThreshold

MonitoringThreshold

Name Description Value
value The threshold value. If null, the set default is dependent on the metric type. int

DataDriftMonitoringSignal

Name Description Value
signalType [Required] Specifies the type of signal to monitor. 'DataDrift' (required)
baselineData [Required] The data to calculate drift against. MonitoringInputData (required)
dataSegment The data segment used for scoping on a subset of the data population. MonitoringDataSegment
features The feature filter which identifies which feature to calculate drift over. MonitoringFeatureFilterBase
metricThresholds [Required] A list of metrics to calculate and their associated thresholds. DataDriftMetricThresholdBase[] (required)
targetData [Required] The data which drift will be calculated for. MonitoringInputData (required)

MonitoringDataSegment

Name Description Value
feature The feature to segment the data on. string
values Filters for only the specified values of the given segmented feature. string[]

MonitoringFeatureFilterBase

Name Description Value
filterType Set the object type AllFeatures
FeatureSubset
TopNByAttribution (required)

AllFeatures

Name Description Value
filterType [Required] Specifies the feature filter to leverage when selecting features to calculate metrics over. 'AllFeatures' (required)

FeatureSubset

Name Description Value
filterType [Required] Specifies the feature filter to leverage when selecting features to calculate metrics over. 'FeatureSubset' (required)
features [Required] The list of features to include. string[] (required)

TopNFeaturesByAttribution

Name Description Value
filterType [Required] Specifies the feature filter to leverage when selecting features to calculate metrics over. 'TopNByAttribution' (required)
top The number of top features to include. int

DataDriftMetricThresholdBase

Name Description Value
threshold The threshold value. If null, a default value will be set depending on the selected metric. MonitoringThreshold
dataType Set the object type Categorical
Numerical (required)

CategoricalDataDriftMetricThreshold

Name Description Value
dataType [Required] Specifies the data type of the metric threshold. 'Categorical' (required)
metric [Required] The categorical data drift metric to calculate. 'JensenShannonDistance'
'PearsonsChiSquaredTest'
'PopulationStabilityIndex' (required)

NumericalDataDriftMetricThreshold

Name Description Value
dataType [Required] Specifies the data type of the metric threshold. 'Numerical' (required)
metric [Required] The numerical data drift metric to calculate. 'JensenShannonDistance'
'NormalizedWassersteinDistance'
'PopulationStabilityIndex'
'TwoSampleKolmogorovSmirnovTest' (required)

DataQualityMonitoringSignal

Name Description Value
signalType [Required] Specifies the type of signal to monitor. 'DataQuality' (required)
baselineData [Required] The data to calculate drift against. MonitoringInputData (required)
features The features to calculate drift over. MonitoringFeatureFilterBase
metricThresholds [Required] A list of metrics to calculate and their associated thresholds. DataQualityMetricThresholdBase[] (required)
targetData [Required] The data produced by the production service which drift will be calculated for. MonitoringInputData (required)

DataQualityMetricThresholdBase

Name Description Value
threshold The threshold value. If null, a default value will be set depending on the selected metric. MonitoringThreshold
dataType Set the object type Categorical
Numerical (required)

CategoricalDataQualityMetricThreshold

Name Description Value
dataType [Required] Specifies the data type of the metric threshold. 'Categorical' (required)
metric [Required] The categorical data quality metric to calculate. 'DataTypeErrorRate'
'NullValueRate'
'OutOfBoundsRate' (required)

NumericalDataQualityMetricThreshold

Name Description Value
dataType [Required] Specifies the data type of the metric threshold. 'Numerical' (required)
metric [Required] The numerical data quality metric to calculate. 'DataTypeErrorRate'
'NullValueRate'
'OutOfBoundsRate' (required)

FeatureAttributionDriftMonitoringSignal

Name Description Value
signalType [Required] Specifies the type of signal to monitor. 'FeatureAttributionDrift' (required)
baselineData [Required] The data to calculate drift against. MonitoringInputData (required)
metricThreshold [Required] A list of metrics to calculate and their associated thresholds. FeatureAttributionMetricThreshold (required)
modelType [Required] The type of task the model performs. 'Classification'
'Regression' (required)
targetData [Required] The data which drift will be calculated for. MonitoringInputData (required)

FeatureAttributionMetricThreshold

Name Description Value
metric [Required] The feature attribution metric to calculate. 'NormalizedDiscountedCumulativeGain' (required)
threshold The threshold value. If null, a default value will be set depending on the selected metric. MonitoringThreshold

ModelPerformanceSignalBase

Name Description Value
signalType [Required] Specifies the type of signal to monitor. 'ModelPerformance' (required)
baselineData [Required] The data to calculate drift against. MonitoringInputData (required)
dataSegment The data segment. MonitoringDataSegment
metricThreshold [Required] A list of metrics to calculate and their associated thresholds. ModelPerformanceMetricThresholdBase (required)
targetData [Required] The data produced by the production service which drift will be calculated for. MonitoringInputData (required)

ModelPerformanceMetricThresholdBase

Name Description Value
threshold The threshold value. If null, a default value will be set depending on the selected metric. MonitoringThreshold
modelType Set the object type Classification
Regression (required)

ClassificationModelPerformanceMetricThreshold

Name Description Value
modelType [Required] Specifies the data type of the metric threshold. 'Classification' (required)
metric [Required] The classification model performance to calculate. 'Accuracy'
'F1Score'
'Precision'
'Recall' (required)

RegressionModelPerformanceMetricThreshold

Name Description Value
modelType [Required] Specifies the data type of the metric threshold. 'Regression' (required)
metric [Required] The regression model performance metric to calculate. 'MeanAbsoluteError'
'MeanSquaredError'
'RootMeanSquaredError' (required)

PredictionDriftMonitoringSignal

Name Description Value
signalType [Required] Specifies the type of signal to monitor. 'PredictionDrift' (required)
baselineData [Required] The data to calculate drift against. MonitoringInputData (required)
metricThresholds [Required] A list of metrics to calculate and their associated thresholds. PredictionDriftMetricThresholdBase[] (required)
modelType [Required] The type of the model monitored. 'Classification'
'Regression' (required)
targetData [Required] The data which drift will be calculated for. MonitoringInputData (required)

PredictionDriftMetricThresholdBase

Name Description Value
threshold The threshold value. If null, a default value will be set depending on the selected metric. MonitoringThreshold
dataType Set the object type Categorical
Numerical (required)

CategoricalPredictionDriftMetricThreshold

Name Description Value
dataType [Required] Specifies the data type of the metric threshold. 'Categorical' (required)
metric [Required] The categorical prediction drift metric to calculate. 'JensenShannonDistance'
'PearsonsChiSquaredTest'
'PopulationStabilityIndex' (required)

NumericalPredictionDriftMetricThreshold

Name Description Value
dataType [Required] Specifies the data type of the metric threshold. 'Numerical' (required)
metric [Required] The numerical prediction drift metric to calculate. 'JensenShannonDistance'
'NormalizedWassersteinDistance'
'PopulationStabilityIndex'
'TwoSampleKolmogorovSmirnovTest' (required)

ImportDataAction

Name Description Value
actionType [Required] Specifies the action type of the schedule 'ImportData' (required)
dataImportDefinition [Required] Defines Schedule action definition details. DataImport (required)

DataImport

Name Description Value
assetName Name of the asset for data import job to create string
autoDeleteSetting Specifies the lifecycle setting of managed data asset. AutoDeleteSetting
dataType [Required] Specifies the type of data. 'mltable'
'uri_file'
'uri_folder' (required)
dataUri [Required] Uri of the data. Example: https://go.microsoft.com/fwlink/?linkid=2202330 string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]
description The asset description text. string
intellectualProperty Intellectual Property details. Used if data is an Intellectual Property. IntellectualProperty
isAnonymous If the name version are system generated (anonymous registration). For types where Stage is defined, when Stage is provided it will be used to populate IsAnonymous bool
isArchived Is the asset archived? For types where Stage is defined, when Stage is provided it will be used to populate IsArchived bool
properties The asset property dictionary. ResourceBaseProperties
source Source data of the asset to import from DataImportSource
stage Stage in the data lifecycle assigned to this data asset string
tags Tag dictionary. Tags can be added, removed, and updated. object

IntellectualProperty

Name Description Value
protectionLevel Protection level of the Intellectual Property. 'All'
'None'
publisher [Required] Publisher of the Intellectual Property. Must be the same as Registry publisher name. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]

DataImportSource

Name Description Value
connection Workspace connection for data import source storage string
sourceType Set the object type database
file_system (required)

DatabaseSource

Name Description Value
sourceType [Required] Specifies the type of data. 'database' (required)
query SQL Query statement for data import Database source string
storedProcedure SQL StoredProcedure on data import Database source string
storedProcedureParams SQL StoredProcedure parameters DatabaseSourceStoredProcedureParamsItem[]
tableName Name of the table on data import Database source string

DatabaseSourceStoredProcedureParamsItem

Name Description Value
{customized property} string

FileSystemSource

Name Description Value
sourceType [Required] Specifies the type of data. 'file_system' (required)
path Path on data import FileSystem source string

EndpointScheduleAction

Name Description Value
actionType [Required] Specifies the action type of the schedule 'InvokeBatchEndpoint' (required)
endpointInvocationDefinition [Required] Defines Schedule action definition details.
{see href="TBD" /}

TriggerBase

Name Description Value
endTime Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer https://en.wikipedia.org/wiki/ISO_8601.
Recommented format would be "2022-06-01T00:00:01"
If not present, the schedule will run indefinitely		 string
startTime Specifies start time of schedule in ISO 8601 format, but without a UTC offset. string
timeZone Specifies time zone in which the schedule runs.
TimeZone should follow Windows time zone format. Refer: /windows-hardware/manufacture/desktop/default-time-zones />		 string
triggerType Set the object type Cron
Recurrence (required)

CronTrigger

Name Description Value
triggerType [Required] 'Cron' (required)
expression [Required] Specifies cron expression of schedule.
The expression should follow NCronTab format.		 string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]

RecurrenceTrigger

Name Description Value
endTime Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer https://en.wikipedia.org/wiki/ISO_8601.
Recommented format would be "2022-06-01T00:00:01"
If not present, the schedule will run indefinitely		 string
frequency [Required] The frequency to trigger schedule. 'Day'
'Hour'
'Minute'
'Month'
'Week' (required)
interval [Required] Specifies schedule interval in conjunction with frequency int (required)
schedule The recurrence schedule. RecurrenceSchedule
startTime Specifies start time of schedule in ISO 8601 format, but without a UTC offset. string
timeZone Specifies time zone in which the schedule runs.
TimeZone should follow Windows time zone format. Refer: /windows-hardware/manufacture/desktop/default-time-zones		 string
triggerType [Required] 'Cron'
'Recurrence' (required)

RecurrenceSchedule

Name Description Value
hours [Required] List of hours for the schedule. int[] (required)
minutes [Required] List of minutes for the schedule. int[] (required)
monthDays List of month days for the schedule int[]
weekDays List of days for the schedule. String array containing any of:
'Friday'
'Monday'
'Saturday'
'Sunday'
'Thursday'
'Tuesday'
'Wednesday'

Terraform (AzAPI provider) resource definition

The workspaces/schedules resource type can be deployed with operations that target:

  • Resource groups

For a list of changed properties in each API version, see change log.

Resource format

To create a Microsoft.MachineLearningServices/workspaces/schedules resource, add the following Terraform to your template.

resource "azapi_resource" "symbolicname" {
  type = "Microsoft.MachineLearningServices/workspaces/schedules@2023-04-01-preview"
  name = "string"
  parent_id = "string"
  body = jsonencode({
    properties = {
      action = {
        actionType = "string"
        // For remaining properties, see ScheduleActionBase objects
      }
      description = "string"
      displayName = "string"
      isEnabled = bool
      properties = {
        {customized property} = "string"
      }
      tags = {}
      trigger = {
        endTime = "string"
        startTime = "string"
        timeZone = "string"
        triggerType = "string"
        // For remaining properties, see TriggerBase objects
      }
    }
  })
}

ScheduleActionBase objects

Set the actionType property to specify the type of object.

For CreateJob, use:

  actionType = "CreateJob"
  jobDefinition = {
    componentId = "string"
    computeId = "string"
    description = "string"
    displayName = "string"
    experimentName = "string"
    identity {
      identityType = "string"
      // For remaining properties, see IdentityConfiguration objects
    }
    isArchived = bool
    notificationSetting = {
      emailOn = [
        "string"
      ]
      emails = [
        "string"
      ]
      webhooks = {
        {customized property} = {
          eventType = "string"
          webhookType = "string"
          // For remaining properties, see Webhook objects
        }
      }
    }
    properties = {
      {customized property} = "string"
    }
    secretsConfiguration = {
      {customized property} = {
        uri = "string"
        workspaceSecretName = "string"
      }
    }
    services = {
      {customized property} = {
        endpoint = "string"
        jobServiceType = "string"
        nodes = {
          nodesValueType = "string"
          // For remaining properties, see Nodes objects
        }
        port = int
        properties = {
          {customized property} = "string"
        }
      }
    }
    tags = {}
    jobType = "string"
    // For remaining properties, see JobBaseProperties objects
  }

For CreateMonitor, use:

  actionType = "CreateMonitor"
  monitorDefinition = {
    alertNotificationSetting = {
      alertNotificationType = "string"
      // For remaining properties, see MonitoringAlertNotificationSettingsBase objects
    }
    computeId = "string"
    monitoringTarget = "string"
    signals = {
      {customized property} = {
        lookbackPeriod = "string"
        mode = "string"
        signalType = "string"
        // For remaining properties, see MonitoringSignalBase objects
      }
    }
  }

For ImportData, use:

  actionType = "ImportData"
  dataImportDefinition = {
    assetName = "string"
    autoDeleteSetting = {
      condition = "string"
      value = "string"
    }
    dataType = "string"
    dataUri = "string"
    description = "string"
    intellectualProperty = {
      protectionLevel = "string"
      publisher = "string"
    }
    isAnonymous = bool
    isArchived = bool
    properties = {
      {customized property} = "string"
    }
    source = {
      connection = "string"
      sourceType = "string"
      // For remaining properties, see DataImportSource objects
    }
    stage = "string"
    tags = {}
  }

For InvokeBatchEndpoint, use:

  actionType = "InvokeBatchEndpoint"

JobBaseProperties objects

Set the jobType property to specify the type of object.

For AutoML, use:

  jobType = "AutoML"
  environmentId = "string"
  environmentVariables = {
    {customized property} = "string"
  }
  outputs = {
    {customized property} = {
      description = "string"
      jobOutputType = "string"
      // For remaining properties, see JobOutput objects
    }
  }
  queueSettings = {
    jobTier = "string"
    priority = int
  }
  resources = {
    dockerArgs = "string"
    instanceCount = int
    instanceType = "string"
    locations = [
      "string"
    ]
    maxInstanceCount = int
    properties = {}
    shmSize = "string"
  }
  taskDetails = {
    logVerbosity = "string"
    targetColumnName = "string"
    trainingData = {
      description = "string"
      jobInputType = "string"
      mode = "string"
      uri = "string"
    }
    taskType = "string"
    // For remaining properties, see AutoMLVertical objects
  }

For Command, use:

  jobType = "Command"
  autologgerSettings = {
    mlflowAutologger = "string"
  }
  codeId = "string"
  command = "string"
  distribution = {
    distributionType = "string"
    // For remaining properties, see DistributionConfiguration objects
  }
  environmentId = "string"
  environmentVariables = {
    {customized property} = "string"
  }
  inputs = {
    {customized property} = {
      description = "string"
      jobInputType = "string"
      // For remaining properties, see JobInput objects
    }
  }
  limits = {
    jobLimitsType = "string"
    timeout = "string"
  }
  outputs = {
    {customized property} = {
      description = "string"
      jobOutputType = "string"
      // For remaining properties, see JobOutput objects
    }
  }
  queueSettings = {
    jobTier = "string"
    priority = int
  }
  resources = {
    dockerArgs = "string"
    instanceCount = int
    instanceType = "string"
    locations = [
      "string"
    ]
    maxInstanceCount = int
    properties = {}
    shmSize = "string"
  }

For Labeling, use:

  jobType = "Labeling"
  dataConfiguration = {
    dataId = "string"
    incrementalDataRefresh = "string"
  }
  jobInstructions = {
    uri = "string"
  }
  labelCategories = {
    {customized property} = {
      classes = {
        {customized property} = {
          displayName = "string"
          subclasses = {
            {customized property} = {}
        }
      }
      displayName = "string"
      multiSelect = "string"
    }
  }
  labelingJobMediaProperties = {
    mediaType = "string"
    // For remaining properties, see LabelingJobMediaProperties objects
  }
  mlAssistConfiguration = {
    mlAssist = "string"
    // For remaining properties, see MLAssistConfiguration objects
  }

For Pipeline, use:

  jobType = "Pipeline"
  inputs = {
    {customized property} = {
      description = "string"
      jobInputType = "string"
      // For remaining properties, see JobInput objects
    }
  }
  jobs = {}
  outputs = {
    {customized property} = {
      description = "string"
      jobOutputType = "string"
      // For remaining properties, see JobOutput objects
    }
  }
  sourceJobId = "string"

For Spark, use:

  jobType = "Spark"
  archives = [
    "string"
  ]
  args = "string"
  codeId = "string"
  conf = {
    {customized property} = "string"
  }
  entry = {
    sparkJobEntryType = "string"
    // For remaining properties, see SparkJobEntry objects
  }
  environmentId = "string"
  files = [
    "string"
  ]
  inputs = {
    {customized property} = {
      description = "string"
      jobInputType = "string"
      // For remaining properties, see JobInput objects
    }
  }
  jars = [
    "string"
  ]
  outputs = {
    {customized property} = {
      description = "string"
      jobOutputType = "string"
      // For remaining properties, see JobOutput objects
    }
  }
  pyFiles = [
    "string"
  ]
  queueSettings = {
    jobTier = "string"
    priority = int
  }
  resources = {
    instanceType = "string"
    runtimeVersion = "string"
  }

For Sweep, use:

  jobType = "Sweep"
  earlyTermination = {
    delayEvaluation = int
    evaluationInterval = int
    policyType = "string"
    // For remaining properties, see EarlyTerminationPolicy objects
  }
  inputs = {
    {customized property} = {
      description = "string"
      jobInputType = "string"
      // For remaining properties, see JobInput objects
    }
  }
  limits = {
    jobLimitsType = "string"
    maxConcurrentTrials = int
    maxTotalTrials = int
    timeout = "string"
    trialTimeout = "string"
  }
  objective = {
    goal = "string"
    primaryMetric = "string"
  }
  outputs = {
    {customized property} = {
      description = "string"
      jobOutputType = "string"
      // For remaining properties, see JobOutput objects
    }
  }
  queueSettings = {
    jobTier = "string"
    priority = int
  }
  samplingAlgorithm = {
    samplingAlgorithmType = "string"
    // For remaining properties, see SamplingAlgorithm objects
  }
  trial = {
    codeId = "string"
    command = "string"
    distribution = {
      distributionType = "string"
      // For remaining properties, see DistributionConfiguration objects
    }
    environmentId = "string"
    environmentVariables = {
      {customized property} = "string"
    }
    resources = {
      dockerArgs = "string"
      instanceCount = int
      instanceType = "string"
      locations = [
        "string"
      ]
      maxInstanceCount = int
      properties = {}
      shmSize = "string"
    }
  }

IdentityConfiguration objects

Set the identityType property to specify the type of object.

For AMLToken, use:

  identityType = "AMLToken"

For Managed, use:

  identityType = "Managed"
  clientId = "string"
  objectId = "string"
  resourceId = "string"

For UserIdentity, use:

  identityType = "UserIdentity"

Webhook objects

Set the webhookType property to specify the type of object.

For AzureDevOps, use:

  webhookType = "AzureDevOps"

Nodes objects

Set the nodesValueType property to specify the type of object.

For All, use:

  nodesValueType = "All"

JobOutput objects

Set the jobOutputType property to specify the type of object.

For custom_model, use:

  jobOutputType = "custom_model"
  assetName = "string"
  assetVersion = "string"
  autoDeleteSetting = {
    condition = "string"
    value = "string"
  }
  mode = "string"
  uri = "string"

For mlflow_model, use:

  jobOutputType = "mlflow_model"
  assetName = "string"
  assetVersion = "string"
  autoDeleteSetting = {
    condition = "string"
    value = "string"
  }
  mode = "string"
  uri = "string"

For mltable, use:

  jobOutputType = "mltable"
  assetName = "string"
  assetVersion = "string"
  autoDeleteSetting = {
    condition = "string"
    value = "string"
  }
  mode = "string"
  uri = "string"

For triton_model, use:

  jobOutputType = "triton_model"
  assetName = "string"
  assetVersion = "string"
  autoDeleteSetting = {
    condition = "string"
    value = "string"
  }
  mode = "string"
  uri = "string"

For uri_file, use:

  jobOutputType = "uri_file"
  assetName = "string"
  assetVersion = "string"
  autoDeleteSetting = {
    condition = "string"
    value = "string"
  }
  mode = "string"
  uri = "string"

For uri_folder, use:

  jobOutputType = "uri_folder"
  assetName = "string"
  assetVersion = "string"
  autoDeleteSetting = {
    condition = "string"
    value = "string"
  }
  mode = "string"
  uri = "string"

AutoMLVertical objects

Set the taskType property to specify the type of object.

For Classification, use:

  taskType = "Classification"
  cvSplitColumnNames = [
    "string"
  ]
  featurizationSettings = {
    blockedTransformers = [
      "string"
    ]
    columnNameAndTypes = {
      {customized property} = "string"
    }
    datasetLanguage = "string"
    enableDnnFeaturization = bool
    mode = "string"
    transformerParams = {
      {customized property} = [
        {
          fields = [
            "string"
          ]
        }
      ]
    }
  }
  fixedParameters = {
    booster = "string"
    boostingType = "string"
    growPolicy = "string"
    learningRate = int
    maxBin = int
    maxDepth = int
    maxLeaves = int
    minDataInLeaf = int
    minSplitGain = int
    modelName = "string"
    nEstimators = int
    numLeaves = int
    preprocessorName = "string"
    regAlpha = int
    regLambda = int
    subsample = int
    subsampleFreq = int
    treeMethod = "string"
    withMean = bool
    withStd = bool
  }
  limitSettings = {
    enableEarlyTermination = bool
    exitScore = int
    maxConcurrentTrials = int
    maxCoresPerTrial = int
    maxNodes = int
    maxTrials = int
    sweepConcurrentTrials = int
    sweepTrials = int
    timeout = "string"
    trialTimeout = "string"
  }
  nCrossValidations = {
    mode = "string"
    // For remaining properties, see NCrossValidations objects
  }
  positiveLabel = "string"
  primaryMetric = "string"
  searchSpace = [
    {
      booster = "string"
      boostingType = "string"
      growPolicy = "string"
      learningRate = "string"
      maxBin = "string"
      maxDepth = "string"
      maxLeaves = "string"
      minDataInLeaf = "string"
      minSplitGain = "string"
      modelName = "string"
      nEstimators = "string"
      numLeaves = "string"
      preprocessorName = "string"
      regAlpha = "string"
      regLambda = "string"
      subsample = "string"
      subsampleFreq = "string"
      treeMethod = "string"
      withMean = "string"
      withStd = "string"
    }
  ]
  sweepSettings = {
    earlyTermination = {
      delayEvaluation = int
      evaluationInterval = int
      policyType = "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm = "string"
  }
  testData = {
    description = "string"
    jobInputType = "string"
    mode = "string"
    uri = "string"
  }
  testDataSize = int
  trainingSettings = {
    allowedTrainingAlgorithms = [
      "string"
    ]
    blockedTrainingAlgorithms = [
      "string"
    ]
    enableDnnTraining = bool
    enableModelExplainability = bool
    enableOnnxCompatibleModels = bool
    enableStackEnsemble = bool
    enableVoteEnsemble = bool
    ensembleModelDownloadTimeout = "string"
    stackEnsembleSettings = {
      stackMetaLearnerTrainPercentage = int
      stackMetaLearnerType = "string"
    }
    trainingMode = "string"
  }
  validationData = {
    description = "string"
    jobInputType = "string"
    mode = "string"
    uri = "string"
  }
  validationDataSize = int
  weightColumnName = "string"

For Forecasting, use:

  taskType = "Forecasting"
  cvSplitColumnNames = [
    "string"
  ]
  featurizationSettings = {
    blockedTransformers = [
      "string"
    ]
    columnNameAndTypes = {
      {customized property} = "string"
    }
    datasetLanguage = "string"
    enableDnnFeaturization = bool
    mode = "string"
    transformerParams = {
      {customized property} = [
        {
          fields = [
            "string"
          ]
        }
      ]
    }
  }
  fixedParameters = {
    booster = "string"
    boostingType = "string"
    growPolicy = "string"
    learningRate = int
    maxBin = int
    maxDepth = int
    maxLeaves = int
    minDataInLeaf = int
    minSplitGain = int
    modelName = "string"
    nEstimators = int
    numLeaves = int
    preprocessorName = "string"
    regAlpha = int
    regLambda = int
    subsample = int
    subsampleFreq = int
    treeMethod = "string"
    withMean = bool
    withStd = bool
  }
  forecastingSettings = {
    countryOrRegionForHolidays = "string"
    cvStepSize = int
    featureLags = "string"
    featuresUnknownAtForecastTime = [
      "string"
    ]
    forecastHorizon = {
      mode = "string"
      // For remaining properties, see ForecastHorizon objects
    }
    frequency = "string"
    seasonality = {
      mode = "string"
      // For remaining properties, see Seasonality objects
    }
    shortSeriesHandlingConfig = "string"
    targetAggregateFunction = "string"
    targetLags = {
      mode = "string"
      // For remaining properties, see TargetLags objects
    }
    targetRollingWindowSize = {
      mode = "string"
      // For remaining properties, see TargetRollingWindowSize objects
    }
    timeColumnName = "string"
    timeSeriesIdColumnNames = [
      "string"
    ]
    useStl = "string"
  }
  limitSettings = {
    enableEarlyTermination = bool
    exitScore = int
    maxConcurrentTrials = int
    maxCoresPerTrial = int
    maxNodes = int
    maxTrials = int
    sweepConcurrentTrials = int
    sweepTrials = int
    timeout = "string"
    trialTimeout = "string"
  }
  nCrossValidations = {
    mode = "string"
    // For remaining properties, see NCrossValidations objects
  }
  primaryMetric = "string"
  searchSpace = [
    {
      booster = "string"
      boostingType = "string"
      growPolicy = "string"
      learningRate = "string"
      maxBin = "string"
      maxDepth = "string"
      maxLeaves = "string"
      minDataInLeaf = "string"
      minSplitGain = "string"
      modelName = "string"
      nEstimators = "string"
      numLeaves = "string"
      preprocessorName = "string"
      regAlpha = "string"
      regLambda = "string"
      subsample = "string"
      subsampleFreq = "string"
      treeMethod = "string"
      withMean = "string"
      withStd = "string"
    }
  ]
  sweepSettings = {
    earlyTermination = {
      delayEvaluation = int
      evaluationInterval = int
      policyType = "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm = "string"
  }
  testData = {
    description = "string"
    jobInputType = "string"
    mode = "string"
    uri = "string"
  }
  testDataSize = int
  trainingSettings = {
    allowedTrainingAlgorithms = [
      "string"
    ]
    blockedTrainingAlgorithms = [
      "string"
    ]
    enableDnnTraining = bool
    enableModelExplainability = bool
    enableOnnxCompatibleModels = bool
    enableStackEnsemble = bool
    enableVoteEnsemble = bool
    ensembleModelDownloadTimeout = "string"
    stackEnsembleSettings = {
      stackMetaLearnerTrainPercentage = int
      stackMetaLearnerType = "string"
    }
    trainingMode = "string"
  }
  validationData = {
    description = "string"
    jobInputType = "string"
    mode = "string"
    uri = "string"
  }
  validationDataSize = int
  weightColumnName = "string"

For ImageClassification, use:

  taskType = "ImageClassification"
  limitSettings = {
    maxConcurrentTrials = int
    maxTrials = int
    timeout = "string"
  }
  modelSettings = {
    advancedSettings = "string"
    amsGradient = bool
    augmentations = "string"
    beta1 = int
    beta2 = int
    checkpointFrequency = int
    checkpointModel = {
      description = "string"
      jobInputType = "string"
      mode = "string"
      uri = "string"
    }
    checkpointRunId = "string"
    distributed = bool
    earlyStopping = bool
    earlyStoppingDelay = int
    earlyStoppingPatience = int
    enableOnnxNormalization = bool
    evaluationFrequency = int
    gradientAccumulationStep = int
    layersToFreeze = int
    learningRate = int
    learningRateScheduler = "string"
    modelName = "string"
    momentum = int
    nesterov = bool
    numberOfEpochs = int
    numberOfWorkers = int
    optimizer = "string"
    randomSeed = int
    stepLRGamma = int
    stepLRStepSize = int
    trainingBatchSize = int
    trainingCropSize = int
    validationBatchSize = int
    validationCropSize = int
    validationResizeSize = int
    warmupCosineLRCycles = int
    warmupCosineLRWarmupEpochs = int
    weightDecay = int
    weightedLoss = int
  }
  primaryMetric = "string"
  searchSpace = [
    {
      amsGradient = "string"
      augmentations = "string"
      beta1 = "string"
      beta2 = "string"
      distributed = "string"
      earlyStopping = "string"
      earlyStoppingDelay = "string"
      earlyStoppingPatience = "string"
      enableOnnxNormalization = "string"
      evaluationFrequency = "string"
      gradientAccumulationStep = "string"
      layersToFreeze = "string"
      learningRate = "string"
      learningRateScheduler = "string"
      modelName = "string"
      momentum = "string"
      nesterov = "string"
      numberOfEpochs = "string"
      numberOfWorkers = "string"
      optimizer = "string"
      randomSeed = "string"
      stepLRGamma = "string"
      stepLRStepSize = "string"
      trainingBatchSize = "string"
      trainingCropSize = "string"
      validationBatchSize = "string"
      validationCropSize = "string"
      validationResizeSize = "string"
      warmupCosineLRCycles = "string"
      warmupCosineLRWarmupEpochs = "string"
      weightDecay = "string"
      weightedLoss = "string"
    }
  ]
  sweepSettings = {
    earlyTermination = {
      delayEvaluation = int
      evaluationInterval = int
      policyType = "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm = "string"
  }
  validationData = {
    description = "string"
    jobInputType = "string"
    mode = "string"
    uri = "string"
  }
  validationDataSize = int

For ImageClassificationMultilabel, use:

  taskType = "ImageClassificationMultilabel"
  limitSettings = {
    maxConcurrentTrials = int
    maxTrials = int
    timeout = "string"
  }
  modelSettings = {
    advancedSettings = "string"
    amsGradient = bool
    augmentations = "string"
    beta1 = int
    beta2 = int
    checkpointFrequency = int
    checkpointModel = {
      description = "string"
      jobInputType = "string"
      mode = "string"
      uri = "string"
    }
    checkpointRunId = "string"
    distributed = bool
    earlyStopping = bool
    earlyStoppingDelay = int
    earlyStoppingPatience = int
    enableOnnxNormalization = bool
    evaluationFrequency = int
    gradientAccumulationStep = int
    layersToFreeze = int
    learningRate = int
    learningRateScheduler = "string"
    modelName = "string"
    momentum = int
    nesterov = bool
    numberOfEpochs = int
    numberOfWorkers = int
    optimizer = "string"
    randomSeed = int
    stepLRGamma = int
    stepLRStepSize = int
    trainingBatchSize = int
    trainingCropSize = int
    validationBatchSize = int
    validationCropSize = int
    validationResizeSize = int
    warmupCosineLRCycles = int
    warmupCosineLRWarmupEpochs = int
    weightDecay = int
    weightedLoss = int
  }
  primaryMetric = "string"
  searchSpace = [
    {
      amsGradient = "string"
      augmentations = "string"
      beta1 = "string"
      beta2 = "string"
      distributed = "string"
      earlyStopping = "string"
      earlyStoppingDelay = "string"
      earlyStoppingPatience = "string"
      enableOnnxNormalization = "string"
      evaluationFrequency = "string"
      gradientAccumulationStep = "string"
      layersToFreeze = "string"
      learningRate = "string"
      learningRateScheduler = "string"
      modelName = "string"
      momentum = "string"
      nesterov = "string"
      numberOfEpochs = "string"
      numberOfWorkers = "string"
      optimizer = "string"
      randomSeed = "string"
      stepLRGamma = "string"
      stepLRStepSize = "string"
      trainingBatchSize = "string"
      trainingCropSize = "string"
      validationBatchSize = "string"
      validationCropSize = "string"
      validationResizeSize = "string"
      warmupCosineLRCycles = "string"
      warmupCosineLRWarmupEpochs = "string"
      weightDecay = "string"
      weightedLoss = "string"
    }
  ]
  sweepSettings = {
    earlyTermination = {
      delayEvaluation = int
      evaluationInterval = int
      policyType = "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm = "string"
  }
  validationData = {
    description = "string"
    jobInputType = "string"
    mode = "string"
    uri = "string"
  }
  validationDataSize = int

For ImageInstanceSegmentation, use:

  taskType = "ImageInstanceSegmentation"
  limitSettings = {
    maxConcurrentTrials = int
    maxTrials = int
    timeout = "string"
  }
  modelSettings = {
    advancedSettings = "string"
    amsGradient = bool
    augmentations = "string"
    beta1 = int
    beta2 = int
    boxDetectionsPerImage = int
    boxScoreThreshold = int
    checkpointFrequency = int
    checkpointModel = {
      description = "string"
      jobInputType = "string"
      mode = "string"
      uri = "string"
    }
    checkpointRunId = "string"
    distributed = bool
    earlyStopping = bool
    earlyStoppingDelay = int
    earlyStoppingPatience = int
    enableOnnxNormalization = bool
    evaluationFrequency = int
    gradientAccumulationStep = int
    imageSize = int
    layersToFreeze = int
    learningRate = int
    learningRateScheduler = "string"
    logTrainingMetrics = "string"
    logValidationLoss = "string"
    maxSize = int
    minSize = int
    modelName = "string"
    modelSize = "string"
    momentum = int
    multiScale = bool
    nesterov = bool
    nmsIouThreshold = int
    numberOfEpochs = int
    numberOfWorkers = int
    optimizer = "string"
    randomSeed = int
    stepLRGamma = int
    stepLRStepSize = int
    tileGridSize = "string"
    tileOverlapRatio = int
    tilePredictionsNmsThreshold = int
    trainingBatchSize = int
    validationBatchSize = int
    validationIouThreshold = int
    validationMetricType = "string"
    warmupCosineLRCycles = int
    warmupCosineLRWarmupEpochs = int
    weightDecay = int
  }
  primaryMetric = "MeanAveragePrecision"
  searchSpace = [
    {
      amsGradient = "string"
      augmentations = "string"
      beta1 = "string"
      beta2 = "string"
      boxDetectionsPerImage = "string"
      boxScoreThreshold = "string"
      distributed = "string"
      earlyStopping = "string"
      earlyStoppingDelay = "string"
      earlyStoppingPatience = "string"
      enableOnnxNormalization = "string"
      evaluationFrequency = "string"
      gradientAccumulationStep = "string"
      imageSize = "string"
      layersToFreeze = "string"
      learningRate = "string"
      learningRateScheduler = "string"
      maxSize = "string"
      minSize = "string"
      modelName = "string"
      modelSize = "string"
      momentum = "string"
      multiScale = "string"
      nesterov = "string"
      nmsIouThreshold = "string"
      numberOfEpochs = "string"
      numberOfWorkers = "string"
      optimizer = "string"
      randomSeed = "string"
      stepLRGamma = "string"
      stepLRStepSize = "string"
      tileGridSize = "string"
      tileOverlapRatio = "string"
      tilePredictionsNmsThreshold = "string"
      trainingBatchSize = "string"
      validationBatchSize = "string"
      validationIouThreshold = "string"
      validationMetricType = "string"
      warmupCosineLRCycles = "string"
      warmupCosineLRWarmupEpochs = "string"
      weightDecay = "string"
    }
  ]
  sweepSettings = {
    earlyTermination = {
      delayEvaluation = int
      evaluationInterval = int
      policyType = "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm = "string"
  }
  validationData = {
    description = "string"
    jobInputType = "string"
    mode = "string"
    uri = "string"
  }
  validationDataSize = int

For ImageObjectDetection, use:

  taskType = "ImageObjectDetection"
  limitSettings = {
    maxConcurrentTrials = int
    maxTrials = int
    timeout = "string"
  }
  modelSettings = {
    advancedSettings = "string"
    amsGradient = bool
    augmentations = "string"
    beta1 = int
    beta2 = int
    boxDetectionsPerImage = int
    boxScoreThreshold = int
    checkpointFrequency = int
    checkpointModel = {
      description = "string"
      jobInputType = "string"
      mode = "string"
      uri = "string"
    }
    checkpointRunId = "string"
    distributed = bool
    earlyStopping = bool
    earlyStoppingDelay = int
    earlyStoppingPatience = int
    enableOnnxNormalization = bool
    evaluationFrequency = int
    gradientAccumulationStep = int
    imageSize = int
    layersToFreeze = int
    learningRate = int
    learningRateScheduler = "string"
    logTrainingMetrics = "string"
    logValidationLoss = "string"
    maxSize = int
    minSize = int
    modelName = "string"
    modelSize = "string"
    momentum = int
    multiScale = bool
    nesterov = bool
    nmsIouThreshold = int
    numberOfEpochs = int
    numberOfWorkers = int
    optimizer = "string"
    randomSeed = int
    stepLRGamma = int
    stepLRStepSize = int
    tileGridSize = "string"
    tileOverlapRatio = int
    tilePredictionsNmsThreshold = int
    trainingBatchSize = int
    validationBatchSize = int
    validationIouThreshold = int
    validationMetricType = "string"
    warmupCosineLRCycles = int
    warmupCosineLRWarmupEpochs = int
    weightDecay = int
  }
  primaryMetric = "MeanAveragePrecision"
  searchSpace = [
    {
      amsGradient = "string"
      augmentations = "string"
      beta1 = "string"
      beta2 = "string"
      boxDetectionsPerImage = "string"
      boxScoreThreshold = "string"
      distributed = "string"
      earlyStopping = "string"
      earlyStoppingDelay = "string"
      earlyStoppingPatience = "string"
      enableOnnxNormalization = "string"
      evaluationFrequency = "string"
      gradientAccumulationStep = "string"
      imageSize = "string"
      layersToFreeze = "string"
      learningRate = "string"
      learningRateScheduler = "string"
      maxSize = "string"
      minSize = "string"
      modelName = "string"
      modelSize = "string"
      momentum = "string"
      multiScale = "string"
      nesterov = "string"
      nmsIouThreshold = "string"
      numberOfEpochs = "string"
      numberOfWorkers = "string"
      optimizer = "string"
      randomSeed = "string"
      stepLRGamma = "string"
      stepLRStepSize = "string"
      tileGridSize = "string"
      tileOverlapRatio = "string"
      tilePredictionsNmsThreshold = "string"
      trainingBatchSize = "string"
      validationBatchSize = "string"
      validationIouThreshold = "string"
      validationMetricType = "string"
      warmupCosineLRCycles = "string"
      warmupCosineLRWarmupEpochs = "string"
      weightDecay = "string"
    }
  ]
  sweepSettings = {
    earlyTermination = {
      delayEvaluation = int
      evaluationInterval = int
      policyType = "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm = "string"
  }
  validationData = {
    description = "string"
    jobInputType = "string"
    mode = "string"
    uri = "string"
  }
  validationDataSize = int

For Regression, use:

  taskType = "Regression"
  cvSplitColumnNames = [
    "string"
  ]
  featurizationSettings = {
    blockedTransformers = [
      "string"
    ]
    columnNameAndTypes = {
      {customized property} = "string"
    }
    datasetLanguage = "string"
    enableDnnFeaturization = bool
    mode = "string"
    transformerParams = {
      {customized property} = [
        {
          fields = [
            "string"
          ]
        }
      ]
    }
  }
  fixedParameters = {
    booster = "string"
    boostingType = "string"
    growPolicy = "string"
    learningRate = int
    maxBin = int
    maxDepth = int
    maxLeaves = int
    minDataInLeaf = int
    minSplitGain = int
    modelName = "string"
    nEstimators = int
    numLeaves = int
    preprocessorName = "string"
    regAlpha = int
    regLambda = int
    subsample = int
    subsampleFreq = int
    treeMethod = "string"
    withMean = bool
    withStd = bool
  }
  limitSettings = {
    enableEarlyTermination = bool
    exitScore = int
    maxConcurrentTrials = int
    maxCoresPerTrial = int
    maxNodes = int
    maxTrials = int
    sweepConcurrentTrials = int
    sweepTrials = int
    timeout = "string"
    trialTimeout = "string"
  }
  nCrossValidations = {
    mode = "string"
    // For remaining properties, see NCrossValidations objects
  }
  primaryMetric = "string"
  searchSpace = [
    {
      booster = "string"
      boostingType = "string"
      growPolicy = "string"
      learningRate = "string"
      maxBin = "string"
      maxDepth = "string"
      maxLeaves = "string"
      minDataInLeaf = "string"
      minSplitGain = "string"
      modelName = "string"
      nEstimators = "string"
      numLeaves = "string"
      preprocessorName = "string"
      regAlpha = "string"
      regLambda = "string"
      subsample = "string"
      subsampleFreq = "string"
      treeMethod = "string"
      withMean = "string"
      withStd = "string"
    }
  ]
  sweepSettings = {
    earlyTermination = {
      delayEvaluation = int
      evaluationInterval = int
      policyType = "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm = "string"
  }
  testData = {
    description = "string"
    jobInputType = "string"
    mode = "string"
    uri = "string"
  }
  testDataSize = int
  trainingSettings = {
    allowedTrainingAlgorithms = [
      "string"
    ]
    blockedTrainingAlgorithms = [
      "string"
    ]
    enableDnnTraining = bool
    enableModelExplainability = bool
    enableOnnxCompatibleModels = bool
    enableStackEnsemble = bool
    enableVoteEnsemble = bool
    ensembleModelDownloadTimeout = "string"
    stackEnsembleSettings = {
      stackMetaLearnerTrainPercentage = int
      stackMetaLearnerType = "string"
    }
    trainingMode = "string"
  }
  validationData = {
    description = "string"
    jobInputType = "string"
    mode = "string"
    uri = "string"
  }
  validationDataSize = int
  weightColumnName = "string"

For TextClassification, use:

  taskType = "TextClassification"
  featurizationSettings = {
    datasetLanguage = "string"
  }
  fixedParameters = {
    gradientAccumulationSteps = int
    learningRate = int
    learningRateScheduler = "string"
    modelName = "string"
    numberOfEpochs = int
    trainingBatchSize = int
    validationBatchSize = int
    warmupRatio = int
    weightDecay = int
  }
  limitSettings = {
    maxConcurrentTrials = int
    maxNodes = int
    maxTrials = int
    timeout = "string"
    trialTimeout = "string"
  }
  primaryMetric = "string"
  searchSpace = [
    {
      gradientAccumulationSteps = "string"
      learningRate = "string"
      learningRateScheduler = "string"
      modelName = "string"
      numberOfEpochs = "string"
      trainingBatchSize = "string"
      validationBatchSize = "string"
      warmupRatio = "string"
      weightDecay = "string"
    }
  ]
  sweepSettings = {
    earlyTermination = {
      delayEvaluation = int
      evaluationInterval = int
      policyType = "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm = "string"
  }
  validationData = {
    description = "string"
    jobInputType = "string"
    mode = "string"
    uri = "string"
  }

For TextClassificationMultilabel, use:

  taskType = "TextClassificationMultilabel"
  featurizationSettings = {
    datasetLanguage = "string"
  }
  fixedParameters = {
    gradientAccumulationSteps = int
    learningRate = int
    learningRateScheduler = "string"
    modelName = "string"
    numberOfEpochs = int
    trainingBatchSize = int
    validationBatchSize = int
    warmupRatio = int
    weightDecay = int
  }
  limitSettings = {
    maxConcurrentTrials = int
    maxNodes = int
    maxTrials = int
    timeout = "string"
    trialTimeout = "string"
  }
  searchSpace = [
    {
      gradientAccumulationSteps = "string"
      learningRate = "string"
      learningRateScheduler = "string"
      modelName = "string"
      numberOfEpochs = "string"
      trainingBatchSize = "string"
      validationBatchSize = "string"
      warmupRatio = "string"
      weightDecay = "string"
    }
  ]
  sweepSettings = {
    earlyTermination = {
      delayEvaluation = int
      evaluationInterval = int
      policyType = "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm = "string"
  }
  validationData = {
    description = "string"
    jobInputType = "string"
    mode = "string"
    uri = "string"
  }

For TextNER, use:

  taskType = "TextNER"
  featurizationSettings = {
    datasetLanguage = "string"
  }
  fixedParameters = {
    gradientAccumulationSteps = int
    learningRate = int
    learningRateScheduler = "string"
    modelName = "string"
    numberOfEpochs = int
    trainingBatchSize = int
    validationBatchSize = int
    warmupRatio = int
    weightDecay = int
  }
  limitSettings = {
    maxConcurrentTrials = int
    maxNodes = int
    maxTrials = int
    timeout = "string"
    trialTimeout = "string"
  }
  searchSpace = [
    {
      gradientAccumulationSteps = "string"
      learningRate = "string"
      learningRateScheduler = "string"
      modelName = "string"
      numberOfEpochs = "string"
      trainingBatchSize = "string"
      validationBatchSize = "string"
      warmupRatio = "string"
      weightDecay = "string"
    }
  ]
  sweepSettings = {
    earlyTermination = {
      delayEvaluation = int
      evaluationInterval = int
      policyType = "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm = "string"
  }
  validationData = {
    description = "string"
    jobInputType = "string"
    mode = "string"
    uri = "string"
  }

NCrossValidations objects

Set the mode property to specify the type of object.

For Auto, use:

  mode = "Auto"

For Custom, use:

  mode = "Custom"
  value = int

EarlyTerminationPolicy objects

Set the policyType property to specify the type of object.

For Bandit, use:

  policyType = "Bandit"
  slackAmount = int
  slackFactor = int

For MedianStopping, use:

  policyType = "MedianStopping"

For TruncationSelection, use:

  policyType = "TruncationSelection"
  truncationPercentage = int

ForecastHorizon objects

Set the mode property to specify the type of object.

For Auto, use:

  mode = "Auto"

For Custom, use:

  mode = "Custom"
  value = int

Seasonality objects

Set the mode property to specify the type of object.

For Auto, use:

  mode = "Auto"

For Custom, use:

  mode = "Custom"
  value = int

TargetLags objects

Set the mode property to specify the type of object.

For Auto, use:

  mode = "Auto"

For Custom, use:

  mode = "Custom"
  values = [
    int
  ]

TargetRollingWindowSize objects

Set the mode property to specify the type of object.

For Auto, use:

  mode = "Auto"

For Custom, use:

  mode = "Custom"
  value = int

DistributionConfiguration objects

Set the distributionType property to specify the type of object.

For Mpi, use:

  distributionType = "Mpi"
  processCountPerInstance = int

For PyTorch, use:

  distributionType = "PyTorch"
  processCountPerInstance = int

For Ray, use:

  distributionType = "Ray"
  address = "string"
  dashboardPort = int
  headNodeAdditionalArgs = "string"
  includeDashboard = bool
  port = int
  workerNodeAdditionalArgs = "string"

For TensorFlow, use:

  distributionType = "TensorFlow"
  parameterServerCount = int
  workerCount = int

JobInput objects

Set the jobInputType property to specify the type of object.

For custom_model, use:

  jobInputType = "custom_model"
  mode = "string"
  uri = "string"

For literal, use:

  jobInputType = "literal"
  value = "string"

For mlflow_model, use:

  jobInputType = "mlflow_model"
  mode = "string"
  uri = "string"

For mltable, use:

  jobInputType = "mltable"
  mode = "string"
  uri = "string"

For triton_model, use:

  jobInputType = "triton_model"
  mode = "string"
  uri = "string"

For uri_file, use:

  jobInputType = "uri_file"
  mode = "string"
  uri = "string"

For uri_folder, use:

  jobInputType = "uri_folder"
  mode = "string"
  uri = "string"

LabelingJobMediaProperties objects

Set the mediaType property to specify the type of object.

For Image, use:

  mediaType = "Image"
  annotationType = "string"

For Text, use:

  mediaType = "Text"
  annotationType = "string"

MLAssistConfiguration objects

Set the mlAssist property to specify the type of object.

For Disabled, use:

  mlAssist = "Disabled"

For Enabled, use:

  mlAssist = "Enabled"
  inferencingComputeBinding = "string"
  trainingComputeBinding = "string"

SparkJobEntry objects

Set the sparkJobEntryType property to specify the type of object.

For SparkJobPythonEntry, use:

  sparkJobEntryType = "SparkJobPythonEntry"
  file = "string"

For SparkJobScalaEntry, use:

  sparkJobEntryType = "SparkJobScalaEntry"
  className = "string"

SamplingAlgorithm objects

Set the samplingAlgorithmType property to specify the type of object.

For Bayesian, use:

  samplingAlgorithmType = "Bayesian"

For Grid, use:

  samplingAlgorithmType = "Grid"

For Random, use:

  samplingAlgorithmType = "Random"
  logbase = "string"
  rule = "string"
  seed = int

MonitoringAlertNotificationSettingsBase objects

Set the alertNotificationType property to specify the type of object.

For AzureMonitor, use:

  alertNotificationType = "AzureMonitor"

For Email, use:

  alertNotificationType = "Email"
  emailNotificationSetting = {
    emailOn = [
      "string"
    ]
    emails = [
      "string"
    ]
    webhooks = {
      {customized property} = {
        eventType = "string"
        webhookType = "string"
        // For remaining properties, see Webhook objects
      }
    }
  }

MonitoringSignalBase objects

Set the signalType property to specify the type of object.

For Custom, use:

  signalType = "Custom"
  componentId = "string"
  inputAssets = {
    {customized property} = {
      dataContext = "string"
      preprocessingComponentId = "string"
      targetColumnName = "string"
    }
  }
  metricThresholds = [
    {
      metric = "string"
      threshold = {
        value = int
      }
    }
  ]

For DataDrift, use:

  signalType = "DataDrift"
  baselineData = {
    dataContext = "string"
    preprocessingComponentId = "string"
    targetColumnName = "string"
  }
  dataSegment = {
    feature = "string"
    values = [
      "string"
    ]
  }
  features = {
    filterType = "string"
    // For remaining properties, see MonitoringFeatureFilterBase objects
  }
  metricThresholds = [
    {
      threshold = {
        value = int
      }
      dataType = "string"
      // For remaining properties, see DataDriftMetricThresholdBase objects
    }
  ]
  targetData = {
    dataContext = "string"
    preprocessingComponentId = "string"
    targetColumnName = "string"
  }

For DataQuality, use:

  signalType = "DataQuality"
  baselineData = {
    dataContext = "string"
    preprocessingComponentId = "string"
    targetColumnName = "string"
  }
  features = {
    filterType = "string"
    // For remaining properties, see MonitoringFeatureFilterBase objects
  }
  metricThresholds = [
    {
      threshold = {
        value = int
      }
      dataType = "string"
      // For remaining properties, see DataQualityMetricThresholdBase objects
    }
  ]
  targetData = {
    dataContext = "string"
    preprocessingComponentId = "string"
    targetColumnName = "string"
  }

For FeatureAttributionDrift, use:

  signalType = "FeatureAttributionDrift"
  baselineData = {
    dataContext = "string"
    preprocessingComponentId = "string"
    targetColumnName = "string"
  }
  metricThreshold = {
    metric = "NormalizedDiscountedCumulativeGain"
    threshold = {
      value = int
    }
  }
  modelType = "string"
  targetData = {
    dataContext = "string"
    preprocessingComponentId = "string"
    targetColumnName = "string"
  }

For ModelPerformance, use:

  signalType = "ModelPerformance"
  baselineData = {
    dataContext = "string"
    preprocessingComponentId = "string"
    targetColumnName = "string"
  }
  dataSegment = {
    feature = "string"
    values = [
      "string"
    ]
  }
  metricThreshold = {
    threshold = {
      value = int
    }
    modelType = "string"
    // For remaining properties, see ModelPerformanceMetricThresholdBase objects
  }
  targetData = {
    dataContext = "string"
    preprocessingComponentId = "string"
    targetColumnName = "string"
  }

For PredictionDrift, use:

  signalType = "PredictionDrift"
  baselineData = {
    dataContext = "string"
    preprocessingComponentId = "string"
    targetColumnName = "string"
  }
  metricThresholds = [
    {
      threshold = {
        value = int
      }
      dataType = "string"
      // For remaining properties, see PredictionDriftMetricThresholdBase objects
    }
  ]
  modelType = "string"
  targetData = {
    dataContext = "string"
    preprocessingComponentId = "string"
    targetColumnName = "string"
  }

MonitoringFeatureFilterBase objects

Set the filterType property to specify the type of object.

For AllFeatures, use:

  filterType = "AllFeatures"

For FeatureSubset, use:

  filterType = "FeatureSubset"
  features = [
    "string"
  ]

For TopNByAttribution, use:

  filterType = "TopNByAttribution"
  top = int

DataDriftMetricThresholdBase objects

Set the dataType property to specify the type of object.

For Categorical, use:

  dataType = "Categorical"
  metric = "string"

For Numerical, use:

  dataType = "Numerical"
  metric = "string"

DataQualityMetricThresholdBase objects

Set the dataType property to specify the type of object.

For Categorical, use:

  dataType = "Categorical"
  metric = "string"

For Numerical, use:

  dataType = "Numerical"
  metric = "string"

ModelPerformanceMetricThresholdBase objects

Set the modelType property to specify the type of object.

For Classification, use:

  modelType = "Classification"
  metric = "string"

For Regression, use:

  modelType = "Regression"
  metric = "string"

PredictionDriftMetricThresholdBase objects

Set the dataType property to specify the type of object.

For Categorical, use:

  dataType = "Categorical"
  metric = "string"

For Numerical, use:

  dataType = "Numerical"
  metric = "string"

DataImportSource objects

Set the sourceType property to specify the type of object.

For database, use:

  sourceType = "database"
  query = "string"
  storedProcedure = "string"
  storedProcedureParams = [
    {
      {customized property} = "string"
    }
  ]
  tableName = "string"

For file_system, use:

  sourceType = "file_system"
  path = "string"

TriggerBase objects

Set the triggerType property to specify the type of object.

For Cron, use:

  triggerType = "Cron"
  expression = "string"

For Recurrence, use:

  triggerType = "Recurrence"
  frequency = "string"
  interval = int
  schedule = {
    hours = [
      int
    ]
    minutes = [
      int
    ]
    monthDays = [
      int
    ]
    weekDays = [
      "string"
    ]
  }

Property values

workspaces/schedules

Name Description Value
type The resource type "Microsoft.MachineLearningServices/workspaces/schedules@2023-04-01-preview"
name The resource name string (required)
parent_id The ID of the resource that is the parent for this resource. ID for resource of type: workspaces
properties [Required] Additional attributes of the entity. ScheduleProperties (required)

ScheduleProperties

Name Description Value
action [Required] Specifies the action of the schedule ScheduleActionBase (required)
description The asset description text. string
displayName Display name of schedule. string
isEnabled Is the schedule enabled? bool
properties The asset property dictionary. ResourceBaseProperties
tags Tag dictionary. Tags can be added, removed, and updated. object
trigger [Required] Specifies the trigger details TriggerBase (required)

ScheduleActionBase

Name Description Value
actionType Set the object type CreateJob
CreateMonitor
ImportData
InvokeBatchEndpoint (required)

JobScheduleAction

Name Description Value
actionType [Required] Specifies the action type of the schedule "CreateJob" (required)
jobDefinition [Required] Defines Schedule action definition details. JobBaseProperties (required)

JobBaseProperties

Name Description Value
componentId ARM resource ID of the component resource. string
computeId ARM resource ID of the compute resource. string
description The asset description text. string
displayName Display name of job. string
experimentName The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment. string
identity Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null.
Defaults to AmlToken if null.		 IdentityConfiguration
isArchived Is the asset archived? bool
notificationSetting Notification setting for the job NotificationSetting
properties The asset property dictionary. ResourceBaseProperties
secretsConfiguration Configuration for secrets to be made available during runtime. JobBaseSecretsConfiguration
services List of JobEndpoints.
For local jobs, a job endpoint will have an endpoint value of FileStreamObject.		 JobBaseServices
tags Tag dictionary. Tags can be added, removed, and updated. object
jobType Set the object type AutoML
Command
Labeling
Pipeline
Spark
Sweep (required)

IdentityConfiguration

Name Description Value
identityType Set the object type AMLToken
Managed
UserIdentity (required)

AmlToken

Name Description Value
identityType [Required] Specifies the type of identity framework. "AMLToken" (required)

ManagedIdentity

Name Description Value
identityType [Required] Specifies the type of identity framework. "Managed" (required)
clientId Specifies a user-assigned identity by client ID. For system-assigned, do not set this field. string

Constraints:
Min length = 36
Max length = 36
Pattern = ^[0-9a-fA-F]{8}-([0-9a-fA-F]{4}-){3}[0-9a-fA-F]{12}$
objectId Specifies a user-assigned identity by object ID. For system-assigned, do not set this field. string

Constraints:
Min length = 36
Max length = 36
Pattern = ^[0-9a-fA-F]{8}-([0-9a-fA-F]{4}-){3}[0-9a-fA-F]{12}$
resourceId Specifies a user-assigned identity by ARM resource ID. For system-assigned, do not set this field. string

UserIdentity

Name Description Value
identityType [Required] Specifies the type of identity framework. "UserIdentity" (required)

NotificationSetting

Name Description Value
emailOn Send email notification to user on specified notification type String array containing any of:
"JobCancelled"
"JobCompleted"
"JobFailed"
emails This is the email recipient list which has a limitation of 499 characters in total concat with comma separator string[]
webhooks Send webhook callback to a service. Key is a user-provided name for the webhook. NotificationSettingWebhooks

NotificationSettingWebhooks

Name Description Value
{customized property} Webhook

Webhook

Name Description Value
eventType Send callback on a specified notification event string
webhookType Set the object type AzureDevOps (required)

AzureDevOpsWebhook

Name Description Value
webhookType [Required] Specifies the type of service to send a callback "AzureDevOps" (required)

ResourceBaseProperties

Name Description Value
{customized property} string

JobBaseSecretsConfiguration

Name Description Value
{customized property} SecretConfiguration

SecretConfiguration

Name Description Value
uri Secret Uri.
Sample Uri : https://myvault.vault.azure.net/secrets/mysecretname/secretversion		 string
workspaceSecretName Name of secret in workspace key vault. string

JobBaseServices

Name Description Value
{customized property} JobService

JobService

Name Description Value
endpoint Url for endpoint. string
jobServiceType Endpoint type. string
nodes Nodes that user would like to start the service on.
If Nodes is not set or set to null, the service will only be started on leader node.		 Nodes
port Port for endpoint set by user. int
properties Additional properties to set on the endpoint. JobServiceProperties

Nodes

Name Description Value
nodesValueType Set the object type All (required)

AllNodes

Name Description Value
nodesValueType [Required] Type of the Nodes value "All" (required)

JobServiceProperties

Name Description Value
{customized property} string

AutoMLJob

Name Description Value
jobType [Required] Specifies the type of job. "AutoML" (required)
environmentId The ARM resource ID of the Environment specification for the job.
This is optional value to provide, if not provided, AutoML will default this to Production AutoML curated environment version when running the job.		 string
environmentVariables Environment variables included in the job. AutoMLJobEnvironmentVariables
outputs Mapping of output data bindings used in the job. AutoMLJobOutputs
queueSettings Queue settings for the job QueueSettings
resources Compute Resource configuration for the job. JobResourceConfiguration
taskDetails [Required] This represents scenario which can be one of Tables/NLP/Image AutoMLVertical (required)

AutoMLJobEnvironmentVariables

Name Description Value
{customized property} string

AutoMLJobOutputs

Name Description Value
{customized property} JobOutput

JobOutput

Name Description Value
description Description for the output. string
jobOutputType Set the object type custom_model
mlflow_model
mltable
triton_model
uri_file
uri_folder (required)

CustomModelJobOutput

Name Description Value
jobOutputType [Required] Specifies the type of job. "custom_model" (required)
assetName Output Asset Name. string
assetVersion Output Asset Version. string
autoDeleteSetting Auto delete setting of output data asset. AutoDeleteSetting
mode Output Asset Delivery Mode. "Direct"
"ReadWriteMount"
"Upload"
uri Output Asset URI. string

AutoDeleteSetting

Name Description Value
condition When to check if an asset is expired "CreatedGreaterThan"
"LastAccessedGreaterThan"
value Expiration condition value. string

MLFlowModelJobOutput

Name Description Value
jobOutputType [Required] Specifies the type of job. "mlflow_model" (required)
assetName Output Asset Name. string
assetVersion Output Asset Version. string
autoDeleteSetting Auto delete setting of output data asset. AutoDeleteSetting
mode Output Asset Delivery Mode. "Direct"
"ReadWriteMount"
"Upload"
uri Output Asset URI. string

MLTableJobOutput

Name Description Value
jobOutputType [Required] Specifies the type of job. "mltable" (required)
assetName Output Asset Name. string
assetVersion Output Asset Version. string
autoDeleteSetting Auto delete setting of output data asset. AutoDeleteSetting
mode Output Asset Delivery Mode. "Direct"
"ReadWriteMount"
"Upload"
uri Output Asset URI. string

TritonModelJobOutput

Name Description Value
jobOutputType [Required] Specifies the type of job. "triton_model" (required)
assetName Output Asset Name. string
assetVersion Output Asset Version. string
autoDeleteSetting Auto delete setting of output data asset. AutoDeleteSetting
mode Output Asset Delivery Mode. "Direct"
"ReadWriteMount"
"Upload"
uri Output Asset URI. string

UriFileJobOutput

Name Description Value
jobOutputType [Required] Specifies the type of job. "uri_file" (required)
assetName Output Asset Name. string
assetVersion Output Asset Version. string
autoDeleteSetting Auto delete setting of output data asset. AutoDeleteSetting
mode Output Asset Delivery Mode. "Direct"
"ReadWriteMount"
"Upload"
uri Output Asset URI. string

UriFolderJobOutput

Name Description Value
jobOutputType [Required] Specifies the type of job. "uri_folder" (required)
assetName Output Asset Name. string
assetVersion Output Asset Version. string
autoDeleteSetting Auto delete setting of output data asset. AutoDeleteSetting
mode Output Asset Delivery Mode. "Direct"
"ReadWriteMount"
"Upload"
uri Output Asset URI. string

QueueSettings

Name Description Value
jobTier Enum to determine the job tier. "Basic"
"Premium"
"Spot"
"Standard"
priority Controls the priority of the job on a compute. int

JobResourceConfiguration

Name Description Value
dockerArgs Extra arguments to pass to the Docker run command. This would override any parameters that have already been set by the system, or in this section. This parameter is only supported for Azure ML compute types. string
instanceCount Optional number of instances or nodes used by the compute target. int
instanceType Optional type of VM used as supported by the compute target. string
locations Locations where the job can run. string[]
maxInstanceCount Optional max allowed number of instances or nodes to be used by the compute target.
For use with elastic training, currently supported by PyTorch distribution type only.		 int
properties Additional properties bag. ResourceConfigurationProperties
shmSize Size of the docker container's shared memory block. This should be in the format of (number)(unit) where number as to be greater than 0 and the unit can be one of b(bytes), k(kilobytes), m(megabytes), or g(gigabytes). string

Constraints:
Pattern = \d+[bBkKmMgG]

ResourceConfigurationProperties

Name Description Value
{customized property}

AutoMLVertical

Name Description Value
logVerbosity Log verbosity for the job. "Critical"
"Debug"
"Error"
"Info"
"NotSet"
"Warning"
targetColumnName Target column name: This is prediction values column.
Also known as label column name in context of classification tasks.		 string
trainingData [Required] Training data input. MLTableJobInput (required)
taskType Set the object type Classification
Forecasting
ImageClassification
ImageClassificationMultilabel
ImageInstanceSegmentation
ImageObjectDetection
Regression
TextClassification
TextClassificationMultilabel
TextNER (required)

MLTableJobInput

Name Description Value
description Description for the input. string
jobInputType [Required] Specifies the type of job. "custom_model"
"literal"
"mlflow_model"
"mltable"
"triton_model"
"uri_file"
"uri_folder" (required)
mode Input Asset Delivery Mode. "Direct"
"Download"
"EvalDownload"
"EvalMount"
"ReadOnlyMount"
"ReadWriteMount"
uri [Required] Input Asset URI. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]

Classification

Name Description Value
taskType [Required] Task type for AutoMLJob. "Classification" (required)
cvSplitColumnNames Columns to use for CVSplit data. string[]
featurizationSettings Featurization inputs needed for AutoML job. TableVerticalFeaturizationSettings
fixedParameters Model/training parameters that will remain constant throughout training. TableFixedParameters
limitSettings Execution constraints for AutoMLJob. TableVerticalLimitSettings
nCrossValidations Number of cross validation folds to be applied on training dataset
when validation dataset is not provided.		 NCrossValidations
positiveLabel Positive label for binary metrics calculation. string
primaryMetric Primary metric for the task. "AUCWeighted"
"Accuracy"
"AveragePrecisionScoreWeighted"
"NormMacroRecall"
"PrecisionScoreWeighted"
searchSpace Search space for sampling different combinations of models and their hyperparameters. TableParameterSubspace[]
sweepSettings Settings for model sweeping and hyperparameter tuning. TableSweepSettings
testData Test data input. MLTableJobInput
testDataSize The fraction of test dataset that needs to be set aside for validation purpose.
Values between (0.0 , 1.0)
Applied when validation dataset is not provided.		 int
trainingSettings Inputs for training phase for an AutoML Job. ClassificationTrainingSettings
validationData Validation data inputs. MLTableJobInput
validationDataSize The fraction of training dataset that needs to be set aside for validation purpose.
Values between (0.0 , 1.0)
Applied when validation dataset is not provided.		 int
weightColumnName The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down. string

TableVerticalFeaturizationSettings

Name Description Value
blockedTransformers These transformers shall not be used in featurization. String array containing any of:
"CatTargetEncoder"
"CountVectorizer"
"HashOneHotEncoder"
"LabelEncoder"
"NaiveBayes"
"OneHotEncoder"
"TextTargetEncoder"
"TfIdf"
"WoETargetEncoder"
"WordEmbedding"
columnNameAndTypes Dictionary of column name and its type (int, float, string, datetime etc). TableVerticalFeaturizationSettingsColumnNameAndTypes
datasetLanguage Dataset language, useful for the text data. string
enableDnnFeaturization Determines whether to use Dnn based featurizers for data featurization. bool
mode Featurization mode - User can keep the default 'Auto' mode and AutoML will take care of necessary transformation of the data in featurization phase.
If 'Off' is selected then no featurization is done.
If 'Custom' is selected then user can specify additional inputs to customize how featurization is done.		 "Auto"
"Custom"
"Off"
transformerParams User can specify additional transformers to be used along with the columns to which it would be applied and parameters for the transformer constructor. TableVerticalFeaturizationSettingsTransformerParams

TableVerticalFeaturizationSettingsColumnNameAndTypes

Name Description Value
{customized property} string

TableVerticalFeaturizationSettingsTransformerParams

Name Description Value
{customized property} ColumnTransformer[]

ColumnTransformer

Name Description Value
fields Fields to apply transformer logic on. string[]
parameters Different properties to be passed to transformer.
Input expected is dictionary of key,value pairs in JSON format.

TableFixedParameters

Name Description Value
booster Specify the boosting type, e.g gbdt for XGBoost. string
boostingType Specify the boosting type, e.g gbdt for LightGBM. string
growPolicy Specify the grow policy, which controls the way new nodes are added to the tree. string
learningRate The learning rate for the training procedure. int
maxBin Specify the Maximum number of discrete bins to bucket continuous features . int
maxDepth Specify the max depth to limit the tree depth explicitly. int
maxLeaves Specify the max leaves to limit the tree leaves explicitly. int
minDataInLeaf The minimum number of data per leaf. int
minSplitGain Minimum loss reduction required to make a further partition on a leaf node of the tree. int
modelName The name of the model to train. string
nEstimators Specify the number of trees (or rounds) in an model. int
numLeaves Specify the number of leaves. int
preprocessorName The name of the preprocessor to use. string
regAlpha L1 regularization term on weights. int
regLambda L2 regularization term on weights. int
subsample Subsample ratio of the training instance. int
subsampleFreq Frequency of subsample. int
treeMethod Specify the tree method. string
withMean If true, center before scaling the data with StandardScalar. bool
withStd If true, scaling the data with Unit Variance with StandardScalar. bool

TableVerticalLimitSettings

Name Description Value
enableEarlyTermination Enable early termination, determines whether or not if AutoMLJob will terminate early if there is no score improvement in last 20 iterations. bool
exitScore Exit score for the AutoML job. int
maxConcurrentTrials Maximum Concurrent iterations. int
maxCoresPerTrial Max cores per iteration. int
maxNodes Maximum nodes to use for the experiment. int
maxTrials Number of iterations. int
sweepConcurrentTrials Number of concurrent sweeping runs that user wants to trigger. int
sweepTrials Number of sweeping runs that user wants to trigger. int
timeout AutoML job timeout. string
trialTimeout Iteration timeout. string

NCrossValidations

Name Description Value
mode Set the object type Auto
Custom (required)

AutoNCrossValidations

Name Description Value
mode [Required] Mode for determining N-Cross validations. "Auto" (required)

CustomNCrossValidations

Name Description Value
mode [Required] Mode for determining N-Cross validations. "Custom" (required)
value [Required] N-Cross validations value. int (required)

TableParameterSubspace

Name Description Value
booster Specify the boosting type, e.g gbdt for XGBoost. string
boostingType Specify the boosting type, e.g gbdt for LightGBM. string
growPolicy Specify the grow policy, which controls the way new nodes are added to the tree. string
learningRate The learning rate for the training procedure. string
maxBin Specify the Maximum number of discrete bins to bucket continuous features . string
maxDepth Specify the max depth to limit the tree depth explicitly. string
maxLeaves Specify the max leaves to limit the tree leaves explicitly. string
minDataInLeaf The minimum number of data per leaf. string
minSplitGain Minimum loss reduction required to make a further partition on a leaf node of the tree. string
modelName The name of the model to train. string
nEstimators Specify the number of trees (or rounds) in an model. string
numLeaves Specify the number of leaves. string
preprocessorName The name of the preprocessor to use. string
regAlpha L1 regularization term on weights. string
regLambda L2 regularization term on weights. string
subsample Subsample ratio of the training instance. string
subsampleFreq Frequency of subsample string
treeMethod Specify the tree method. string
withMean If true, center before scaling the data with StandardScalar. string
withStd If true, scaling the data with Unit Variance with StandardScalar. string

TableSweepSettings

Name Description Value
earlyTermination Type of early termination policy for the sweeping job. EarlyTerminationPolicy
samplingAlgorithm [Required] Type of sampling algorithm. "Bayesian"
"Grid"
"Random" (required)

EarlyTerminationPolicy

Name Description Value
delayEvaluation Number of intervals by which to delay the first evaluation. int
evaluationInterval Interval (number of runs) between policy evaluations. int
policyType Set the object type Bandit
MedianStopping
TruncationSelection (required)

BanditPolicy

Name Description Value
policyType [Required] Name of policy configuration "Bandit" (required)
slackAmount Absolute distance allowed from the best performing run. int
slackFactor Ratio of the allowed distance from the best performing run. int

MedianStoppingPolicy

Name Description Value
policyType [Required] Name of policy configuration "MedianStopping" (required)

TruncationSelectionPolicy

Name Description Value
policyType [Required] Name of policy configuration "TruncationSelection" (required)
truncationPercentage The percentage of runs to cancel at each evaluation interval. int

ClassificationTrainingSettings

Name Description Value
allowedTrainingAlgorithms Allowed models for classification task. String array containing any of:
"BernoulliNaiveBayes"
"DecisionTree"
"ExtremeRandomTrees"
"GradientBoosting"
"KNN"
"LightGBM"
"LinearSVM"
"LogisticRegression"
"MultinomialNaiveBayes"
"RandomForest"
"SGD"
"SVM"
"XGBoostClassifier"
blockedTrainingAlgorithms Blocked models for classification task. String array containing any of:
"BernoulliNaiveBayes"
"DecisionTree"
"ExtremeRandomTrees"
"GradientBoosting"
"KNN"
"LightGBM"
"LinearSVM"
"LogisticRegression"
"MultinomialNaiveBayes"
"RandomForest"
"SGD"
"SVM"
"XGBoostClassifier"
enableDnnTraining Enable recommendation of DNN models. bool
enableModelExplainability Flag to turn on explainability on best model. bool
enableOnnxCompatibleModels Flag for enabling onnx compatible models. bool
enableStackEnsemble Enable stack ensemble run. bool
enableVoteEnsemble Enable voting ensemble run. bool
ensembleModelDownloadTimeout During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded.
Configure this parameter with a higher value than 300 secs, if more time is needed.		 string
stackEnsembleSettings Stack ensemble settings for stack ensemble run. StackEnsembleSettings
trainingMode TrainingMode mode - Setting to 'auto' is same as setting it to 'non-distributed' for now, however in the future may result in mixed mode or heuristics based mode selection. Default is 'auto'.
If 'Distributed' then only distributed featurization is used and distributed algorithms are chosen.
If 'NonDistributed' then only non distributed algorithms are chosen.
 "Auto"
"Distributed"
"NonDistributed"

StackEnsembleSettings

Name Description Value
stackMetaLearnerKWargs Optional parameters to pass to the initializer of the meta-learner.
stackMetaLearnerTrainPercentage Specifies the proportion of the training set (when choosing train and validation type of training) to be reserved for training the meta-learner. Default value is 0.2. int
stackMetaLearnerType The meta-learner is a model trained on the output of the individual heterogeneous models. "ElasticNet"
"ElasticNetCV"
"LightGBMClassifier"
"LightGBMRegressor"
"LinearRegression"
"LogisticRegression"
"LogisticRegressionCV"
"None"

Forecasting

Name Description Value
taskType [Required] Task type for AutoMLJob. "Forecasting" (required)
cvSplitColumnNames Columns to use for CVSplit data. string[]
featurizationSettings Featurization inputs needed for AutoML job. TableVerticalFeaturizationSettings
fixedParameters Model/training parameters that will remain constant throughout training. TableFixedParameters
forecastingSettings Forecasting task specific inputs. ForecastingSettings
limitSettings Execution constraints for AutoMLJob. TableVerticalLimitSettings
nCrossValidations Number of cross validation folds to be applied on training dataset
when validation dataset is not provided.		 NCrossValidations
primaryMetric Primary metric for forecasting task. "NormalizedMeanAbsoluteError"
"NormalizedRootMeanSquaredError"
"R2Score"
"SpearmanCorrelation"
searchSpace Search space for sampling different combinations of models and their hyperparameters. TableParameterSubspace[]
sweepSettings Settings for model sweeping and hyperparameter tuning. TableSweepSettings
testData Test data input. MLTableJobInput
testDataSize The fraction of test dataset that needs to be set aside for validation purpose.
Values between (0.0 , 1.0)
Applied when validation dataset is not provided.		 int
trainingSettings Inputs for training phase for an AutoML Job. ForecastingTrainingSettings
validationData Validation data inputs. MLTableJobInput
validationDataSize The fraction of training dataset that needs to be set aside for validation purpose.
Values between (0.0 , 1.0)
Applied when validation dataset is not provided.		 int
weightColumnName The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down. string

ForecastingSettings

Name Description Value
countryOrRegionForHolidays Country or region for holidays for forecasting tasks.
These should be ISO 3166 two-letter country/region codes, for example 'US' or 'GB'.		 string
cvStepSize Number of periods between the origin time of one CV fold and the next fold. For
example, if CVStepSize = 3 for daily data, the origin time for each fold will be
three days apart.		 int
featureLags Flag for generating lags for the numeric features with 'auto' or null. "Auto"
"None"
featuresUnknownAtForecastTime The feature columns that are available for training but unknown at the time of forecast/inference.
If features_unknown_at_forecast_time is not set, it is assumed that all the feature columns in the dataset are known at inference time.		 string[]
forecastHorizon The desired maximum forecast horizon in units of time-series frequency. ForecastHorizon
frequency When forecasting, this parameter represents the period with which the forecast is desired, for example daily, weekly, yearly, etc. The forecast frequency is dataset frequency by default. string
seasonality Set time series seasonality as an integer multiple of the series frequency.
If seasonality is set to 'auto', it will be inferred.		 Seasonality
shortSeriesHandlingConfig The parameter defining how if AutoML should handle short time series. "Auto"
"Drop"
"None"
"Pad"
targetAggregateFunction The function to be used to aggregate the time series target column to conform to a user specified frequency.
If the TargetAggregateFunction is set i.e. not 'None', but the freq parameter is not set, the error is raised. The possible target aggregation functions are: "sum", "max", "min" and "mean".		 "Max"
"Mean"
"Min"
"None"
"Sum"
targetLags The number of past periods to lag from the target column. TargetLags
targetRollingWindowSize The number of past periods used to create a rolling window average of the target column. TargetRollingWindowSize
timeColumnName The name of the time column. This parameter is required when forecasting to specify the datetime column in the input data used for building the time series and inferring its frequency. string
timeSeriesIdColumnNames The names of columns used to group a timeseries. It can be used to create multiple series.
If grain is not defined, the data set is assumed to be one time-series. This parameter is used with task type forecasting.		 string[]
useStl Configure STL Decomposition of the time-series target column. "None"
"Season"
"SeasonTrend"

ForecastHorizon

Name Description Value
mode Set the object type Auto
Custom (required)

AutoForecastHorizon

Name Description Value
mode [Required] Set forecast horizon value selection mode. "Auto" (required)

CustomForecastHorizon

Name Description Value
mode [Required] Set forecast horizon value selection mode. "Custom" (required)
value [Required] Forecast horizon value. int (required)

Seasonality

Name Description Value
mode Set the object type Auto
Custom (required)

AutoSeasonality

Name Description Value
mode [Required] Seasonality mode. "Auto" (required)

CustomSeasonality

Name Description Value
mode [Required] Seasonality mode. "Custom" (required)
value [Required] Seasonality value. int (required)

TargetLags

Name Description Value
mode Set the object type Auto
Custom (required)

AutoTargetLags

Name Description Value
mode [Required] Set target lags mode - Auto/Custom "Auto" (required)

CustomTargetLags

Name Description Value
mode [Required] Set target lags mode - Auto/Custom "Custom" (required)
values [Required] Set target lags values. int[] (required)

TargetRollingWindowSize

Name Description Value
mode Set the object type Auto
Custom (required)

AutoTargetRollingWindowSize

Name Description Value
mode [Required] TargetRollingWindowSiz detection mode. "Auto" (required)

CustomTargetRollingWindowSize

Name Description Value
mode [Required] TargetRollingWindowSiz detection mode. "Custom" (required)
value [Required] TargetRollingWindowSize value. int (required)

ForecastingTrainingSettings

Name Description Value
allowedTrainingAlgorithms Allowed models for forecasting task. String array containing any of:
"Arimax"
"AutoArima"
"Average"
"DecisionTree"
"ElasticNet"
"ExponentialSmoothing"
"ExtremeRandomTrees"
"GradientBoosting"
"KNN"
"LassoLars"
"LightGBM"
"Naive"
"Prophet"
"RandomForest"
"SGD"
"SeasonalAverage"
"SeasonalNaive"
"TCNForecaster"
"XGBoostRegressor"
blockedTrainingAlgorithms Blocked models for forecasting task. String array containing any of:
"Arimax"
"AutoArima"
"Average"
"DecisionTree"
"ElasticNet"
"ExponentialSmoothing"
"ExtremeRandomTrees"
"GradientBoosting"
"KNN"
"LassoLars"
"LightGBM"
"Naive"
"Prophet"
"RandomForest"
"SGD"
"SeasonalAverage"
"SeasonalNaive"
"TCNForecaster"
"XGBoostRegressor"
enableDnnTraining Enable recommendation of DNN models. bool
enableModelExplainability Flag to turn on explainability on best model. bool
enableOnnxCompatibleModels Flag for enabling onnx compatible models. bool
enableStackEnsemble Enable stack ensemble run. bool
enableVoteEnsemble Enable voting ensemble run. bool
ensembleModelDownloadTimeout During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded.
Configure this parameter with a higher value than 300 secs, if more time is needed.		 string
stackEnsembleSettings Stack ensemble settings for stack ensemble run. StackEnsembleSettings
trainingMode TrainingMode mode - Setting to 'auto' is same as setting it to 'non-distributed' for now, however in the future may result in mixed mode or heuristics based mode selection. Default is 'auto'.
If 'Distributed' then only distributed featurization is used and distributed algorithms are chosen.
If 'NonDistributed' then only non distributed algorithms are chosen.
 "Auto"
"Distributed"
"NonDistributed"

ImageClassification

Name Description Value
taskType [Required] Task type for AutoMLJob. "ImageClassification" (required)
limitSettings [Required] Limit settings for the AutoML job. ImageLimitSettings (required)
modelSettings Settings used for training the model. ImageModelSettingsClassification
primaryMetric Primary metric to optimize for this task. "AUCWeighted"
"Accuracy"
"AveragePrecisionScoreWeighted"
"NormMacroRecall"
"PrecisionScoreWeighted"
searchSpace Search space for sampling different combinations of models and their hyperparameters. ImageModelDistributionSettingsClassification[]
sweepSettings Model sweeping and hyperparameter sweeping related settings. ImageSweepSettings
validationData Validation data inputs. MLTableJobInput
validationDataSize The fraction of training dataset that needs to be set aside for validation purpose.
Values between (0.0 , 1.0)
Applied when validation dataset is not provided.
 int

ImageLimitSettings

Name Description Value
maxConcurrentTrials Maximum number of concurrent AutoML iterations. int
maxTrials Maximum number of AutoML iterations. int
timeout AutoML job timeout. string

ImageModelSettingsClassification

Name Description Value
advancedSettings Settings for advanced scenarios. string
amsGradient Enable AMSGrad when optimizer is 'adam' or 'adamw'. bool
augmentations Settings for using Augmentations. string
beta1 Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. int
beta2 Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. int
checkpointFrequency Frequency to store model checkpoints. Must be a positive integer. int
checkpointModel The pretrained checkpoint model for incremental training. MLFlowModelJobInput
checkpointRunId The id of a previous run that has a pretrained checkpoint for incremental training. string
distributed Whether to use distributed training. bool
earlyStopping Enable early stopping logic during training. bool
earlyStoppingDelay Minimum number of epochs or validation evaluations to wait before primary metric improvement
is tracked for early stopping. Must be a positive integer.		 int
earlyStoppingPatience Minimum number of epochs or validation evaluations with no primary metric improvement before
the run is stopped. Must be a positive integer.		 int
enableOnnxNormalization Enable normalization when exporting ONNX model. bool
evaluationFrequency Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. int
gradientAccumulationStep Gradient accumulation means running a configured number of "GradAccumulationStep" steps without
updating the model weights while accumulating the gradients of those steps, and then using
the accumulated gradients to compute the weight updates. Must be a positive integer.		 int
layersToFreeze Number of layers to freeze for the model. Must be a positive integer.
For instance, passing 2 as value for 'seresnext' means
freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please
see: /azure/machine-learning/how-to-auto-train-image-models.		 int
learningRate Initial learning rate. Must be a float in the range [0, 1]. int
learningRateScheduler Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. "None"
"Step"
"WarmupCosine"
modelName Name of the model to use for training.
For more information on the available models please visit the official documentation:
/azure/machine-learning/how-to-auto-train-image-models.		 string
momentum Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. int
nesterov Enable nesterov when optimizer is 'sgd'. bool
numberOfEpochs Number of training epochs. Must be a positive integer. int
numberOfWorkers Number of data loader workers. Must be a non-negative integer. int
optimizer Type of optimizer. "Adam"
"Adamw"
"None"
"Sgd"
randomSeed Random seed to be used when using deterministic training. int
stepLRGamma Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. int
stepLRStepSize Value of step size when learning rate scheduler is 'step'. Must be a positive integer. int
trainingBatchSize Training batch size. Must be a positive integer. int
trainingCropSize Image crop size that is input to the neural network for the training dataset. Must be a positive integer. int
validationBatchSize Validation batch size. Must be a positive integer. int
validationCropSize Image crop size that is input to the neural network for the validation dataset. Must be a positive integer. int
validationResizeSize Image size to which to resize before cropping for validation dataset. Must be a positive integer. int
warmupCosineLRCycles Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. int
warmupCosineLRWarmupEpochs Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. int
weightDecay Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. int
weightedLoss Weighted loss. The accepted values are 0 for no weighted loss.
1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.
 int

MLFlowModelJobInput

Name Description Value
description Description for the input. string
jobInputType [Required] Specifies the type of job. "custom_model"
"literal"
"mlflow_model"
"mltable"
"triton_model"
"uri_file"
"uri_folder" (required)
mode Input Asset Delivery Mode. "Direct"
"Download"
"EvalDownload"
"EvalMount"
"ReadOnlyMount"
"ReadWriteMount"
uri [Required] Input Asset URI. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]

ImageModelDistributionSettingsClassification

Name Description Value
amsGradient Enable AMSGrad when optimizer is 'adam' or 'adamw'. string
augmentations Settings for using Augmentations. string
beta1 Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. string
beta2 Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. string
distributed Whether to use distributer training. string
earlyStopping Enable early stopping logic during training. string
earlyStoppingDelay Minimum number of epochs or validation evaluations to wait before primary metric improvement
is tracked for early stopping. Must be a positive integer.		 string
earlyStoppingPatience Minimum number of epochs or validation evaluations with no primary metric improvement before
the run is stopped. Must be a positive integer.		 string
enableOnnxNormalization Enable normalization when exporting ONNX model. string
evaluationFrequency Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. string
gradientAccumulationStep Gradient accumulation means running a configured number of "GradAccumulationStep" steps without
updating the model weights while accumulating the gradients of those steps, and then using
the accumulated gradients to compute the weight updates. Must be a positive integer.		 string
layersToFreeze Number of layers to freeze for the model. Must be a positive integer.
For instance, passing 2 as value for 'seresnext' means
freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please
see: /azure/machine-learning/how-to-auto-train-image-models.		 string
learningRate Initial learning rate. Must be a float in the range [0, 1]. string
learningRateScheduler Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. string
modelName Name of the model to use for training.
For more information on the available models please visit the official documentation:
/azure/machine-learning/how-to-auto-train-image-models.		 string
momentum Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. string
nesterov Enable nesterov when optimizer is 'sgd'. string
numberOfEpochs Number of training epochs. Must be a positive integer. string
numberOfWorkers Number of data loader workers. Must be a non-negative integer. string
optimizer Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'. string
randomSeed Random seed to be used when using deterministic training. string
stepLRGamma Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. string
stepLRStepSize Value of step size when learning rate scheduler is 'step'. Must be a positive integer. string
trainingBatchSize Training batch size. Must be a positive integer. string
trainingCropSize Image crop size that is input to the neural network for the training dataset. Must be a positive integer. string
validationBatchSize Validation batch size. Must be a positive integer. string
validationCropSize Image crop size that is input to the neural network for the validation dataset. Must be a positive integer. string
validationResizeSize Image size to which to resize before cropping for validation dataset. Must be a positive integer. string
warmupCosineLRCycles Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. string
warmupCosineLRWarmupEpochs Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. string
weightDecay Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. string
weightedLoss Weighted loss. The accepted values are 0 for no weighted loss.
1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.
 string

ImageSweepSettings

Name Description Value
earlyTermination Type of early termination policy. EarlyTerminationPolicy
samplingAlgorithm [Required] Type of the hyperparameter sampling algorithms. "Bayesian"
"Grid"
"Random" (required)

ImageClassificationMultilabel

Name Description Value
taskType [Required] Task type for AutoMLJob. "ImageClassificationMultilabel" (required)
limitSettings [Required] Limit settings for the AutoML job. ImageLimitSettings (required)
modelSettings Settings used for training the model. ImageModelSettingsClassification
primaryMetric Primary metric to optimize for this task. "AUCWeighted"
"Accuracy"
"AveragePrecisionScoreWeighted"
"IOU"
"NormMacroRecall"
"PrecisionScoreWeighted"
searchSpace Search space for sampling different combinations of models and their hyperparameters. ImageModelDistributionSettingsClassification[]
sweepSettings Model sweeping and hyperparameter sweeping related settings. ImageSweepSettings
validationData Validation data inputs. MLTableJobInput
validationDataSize The fraction of training dataset that needs to be set aside for validation purpose.
Values between (0.0 , 1.0)
Applied when validation dataset is not provided.
 int

ImageInstanceSegmentation

Name Description Value
taskType [Required] Task type for AutoMLJob. "ImageInstanceSegmentation" (required)
limitSettings [Required] Limit settings for the AutoML job. ImageLimitSettings (required)
modelSettings Settings used for training the model. ImageModelSettingsObjectDetection
primaryMetric Primary metric to optimize for this task. "MeanAveragePrecision"
searchSpace Search space for sampling different combinations of models and their hyperparameters. ImageModelDistributionSettingsObjectDetection[]
sweepSettings Model sweeping and hyperparameter sweeping related settings. ImageSweepSettings
validationData Validation data inputs. MLTableJobInput
validationDataSize The fraction of training dataset that needs to be set aside for validation purpose.
Values between (0.0 , 1.0)
Applied when validation dataset is not provided.
 int

ImageModelSettingsObjectDetection

Name Description Value
advancedSettings Settings for advanced scenarios. string
amsGradient Enable AMSGrad when optimizer is 'adam' or 'adamw'. bool
augmentations Settings for using Augmentations. string
beta1 Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. int
beta2 Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. int
boxDetectionsPerImage Maximum number of detections per image, for all classes. Must be a positive integer.
Note: This settings is not supported for the 'yolov5' algorithm.		 int
boxScoreThreshold During inference, only return proposals with a classification score greater than
BoxScoreThreshold. Must be a float in the range[0, 1].		 int
checkpointFrequency Frequency to store model checkpoints. Must be a positive integer. int
checkpointModel The pretrained checkpoint model for incremental training. MLFlowModelJobInput
checkpointRunId The id of a previous run that has a pretrained checkpoint for incremental training. string
distributed Whether to use distributed training. bool
earlyStopping Enable early stopping logic during training. bool
earlyStoppingDelay Minimum number of epochs or validation evaluations to wait before primary metric improvement
is tracked for early stopping. Must be a positive integer.		 int
earlyStoppingPatience Minimum number of epochs or validation evaluations with no primary metric improvement before
the run is stopped. Must be a positive integer.		 int
enableOnnxNormalization Enable normalization when exporting ONNX model. bool
evaluationFrequency Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. int
gradientAccumulationStep Gradient accumulation means running a configured number of "GradAccumulationStep" steps without
updating the model weights while accumulating the gradients of those steps, and then using
the accumulated gradients to compute the weight updates. Must be a positive integer.		 int
imageSize Image size for train and validation. Must be a positive integer.
Note: The training run may get into CUDA OOM if the size is too big.
Note: This settings is only supported for the 'yolov5' algorithm.		 int
layersToFreeze Number of layers to freeze for the model. Must be a positive integer.
For instance, passing 2 as value for 'seresnext' means
freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please
see: /azure/machine-learning/how-to-auto-train-image-models.		 int
learningRate Initial learning rate. Must be a float in the range [0, 1]. int
learningRateScheduler Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. "None"
"Step"
"WarmupCosine"
logTrainingMetrics Enable computing and logging training metrics. "Disable"
"Enable"
logValidationLoss Enable computing and logging validation loss. "Disable"
"Enable"
maxSize Maximum size of the image to be rescaled before feeding it to the backbone.
Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
Note: This settings is not supported for the 'yolov5' algorithm.		 int
minSize Minimum size of the image to be rescaled before feeding it to the backbone.
Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
Note: This settings is not supported for the 'yolov5' algorithm.		 int
modelName Name of the model to use for training.
For more information on the available models please visit the official documentation:
/azure/machine-learning/how-to-auto-train-image-models.		 string
modelSize Model size. Must be 'small', 'medium', 'large', or 'xlarge'.
Note: training run may get into CUDA OOM if the model size is too big.
Note: This settings is only supported for the 'yolov5' algorithm.		 "ExtraLarge"
"Large"
"Medium"
"None"
"Small"
momentum Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. int
multiScale Enable multi-scale image by varying image size by +/- 50%.
Note: training run may get into CUDA OOM if no sufficient GPU memory.
Note: This settings is only supported for the 'yolov5' algorithm.		 bool
nesterov Enable nesterov when optimizer is 'sgd'. bool
nmsIouThreshold IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1]. int
numberOfEpochs Number of training epochs. Must be a positive integer. int
numberOfWorkers Number of data loader workers. Must be a non-negative integer. int
optimizer Type of optimizer. "Adam"
"Adamw"
"None"
"Sgd"
randomSeed Random seed to be used when using deterministic training. int
stepLRGamma Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. int
stepLRStepSize Value of step size when learning rate scheduler is 'step'. Must be a positive integer. int
tileGridSize The grid size to use for tiling each image. Note: TileGridSize must not be
None to enable small object detection logic. A string containing two integers in mxn format.
Note: This settings is not supported for the 'yolov5' algorithm.		 string
tileOverlapRatio Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1).
Note: This settings is not supported for the 'yolov5' algorithm.		 int
tilePredictionsNmsThreshold The IOU threshold to use to perform NMS while merging predictions from tiles and image.
Used in validation/ inference. Must be float in the range [0, 1].
Note: This settings is not supported for the 'yolov5' algorithm.		 int
trainingBatchSize Training batch size. Must be a positive integer. int
validationBatchSize Validation batch size. Must be a positive integer. int
validationIouThreshold IOU threshold to use when computing validation metric. Must be float in the range [0, 1]. int
validationMetricType Metric computation method to use for validation metrics. "Coco"
"CocoVoc"
"None"
"Voc"
warmupCosineLRCycles Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. int
warmupCosineLRWarmupEpochs Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. int
weightDecay Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. int

ImageModelDistributionSettingsObjectDetection

Name Description Value
amsGradient Enable AMSGrad when optimizer is 'adam' or 'adamw'. string
augmentations Settings for using Augmentations. string
beta1 Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. string
beta2 Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. string
boxDetectionsPerImage Maximum number of detections per image, for all classes. Must be a positive integer.
Note: This settings is not supported for the 'yolov5' algorithm.		 string
boxScoreThreshold During inference, only return proposals with a classification score greater than
BoxScoreThreshold. Must be a float in the range[0, 1].		 string
distributed Whether to use distributer training. string
earlyStopping Enable early stopping logic during training. string
earlyStoppingDelay Minimum number of epochs or validation evaluations to wait before primary metric improvement
is tracked for early stopping. Must be a positive integer.		 string
earlyStoppingPatience Minimum number of epochs or validation evaluations with no primary metric improvement before
the run is stopped. Must be a positive integer.		 string
enableOnnxNormalization Enable normalization when exporting ONNX model. string
evaluationFrequency Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. string
gradientAccumulationStep Gradient accumulation means running a configured number of "GradAccumulationStep" steps without
updating the model weights while accumulating the gradients of those steps, and then using
the accumulated gradients to compute the weight updates. Must be a positive integer.		 string
imageSize Image size for train and validation. Must be a positive integer.
Note: The training run may get into CUDA OOM if the size is too big.
Note: This settings is only supported for the 'yolov5' algorithm.		 string
layersToFreeze Number of layers to freeze for the model. Must be a positive integer.
For instance, passing 2 as value for 'seresnext' means
freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please
see: /azure/machine-learning/how-to-auto-train-image-models.		 string
learningRate Initial learning rate. Must be a float in the range [0, 1]. string
learningRateScheduler Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. string
maxSize Maximum size of the image to be rescaled before feeding it to the backbone.
Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
Note: This settings is not supported for the 'yolov5' algorithm.		 string
minSize Minimum size of the image to be rescaled before feeding it to the backbone.
Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
Note: This settings is not supported for the 'yolov5' algorithm.		 string
modelName Name of the model to use for training.
For more information on the available models please visit the official documentation:
/azure/machine-learning/how-to-auto-train-image-models.		 string
modelSize Model size. Must be 'small', 'medium', 'large', or 'xlarge'.
Note: training run may get into CUDA OOM if the model size is too big.
Note: This settings is only supported for the 'yolov5' algorithm.		 string
momentum Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. string
multiScale Enable multi-scale image by varying image size by +/- 50%.
Note: training run may get into CUDA OOM if no sufficient GPU memory.
Note: This settings is only supported for the 'yolov5' algorithm.		 string
nesterov Enable nesterov when optimizer is 'sgd'. string
nmsIouThreshold IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1]. string
numberOfEpochs Number of training epochs. Must be a positive integer. string
numberOfWorkers Number of data loader workers. Must be a non-negative integer. string
optimizer Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'. string
randomSeed Random seed to be used when using deterministic training. string
stepLRGamma Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. string
stepLRStepSize Value of step size when learning rate scheduler is 'step'. Must be a positive integer. string
tileGridSize The grid size to use for tiling each image. Note: TileGridSize must not be
None to enable small object detection logic. A string containing two integers in mxn format.
Note: This settings is not supported for the 'yolov5' algorithm.		 string
tileOverlapRatio Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1).
Note: This settings is not supported for the 'yolov5' algorithm.		 string
tilePredictionsNmsThreshold The IOU threshold to use to perform NMS while merging predictions from tiles and image.
Used in validation/ inference. Must be float in the range [0, 1].
Note: This settings is not supported for the 'yolov5' algorithm.
NMS: Non-maximum suppression		 string
trainingBatchSize Training batch size. Must be a positive integer. string
validationBatchSize Validation batch size. Must be a positive integer. string
validationIouThreshold IOU threshold to use when computing validation metric. Must be float in the range [0, 1]. string
validationMetricType Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'. string
warmupCosineLRCycles Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. string
warmupCosineLRWarmupEpochs Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. string
weightDecay Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. string

ImageObjectDetection

Name Description Value
taskType [Required] Task type for AutoMLJob. "ImageObjectDetection" (required)
limitSettings [Required] Limit settings for the AutoML job. ImageLimitSettings (required)
modelSettings Settings used for training the model. ImageModelSettingsObjectDetection
primaryMetric Primary metric to optimize for this task. "MeanAveragePrecision"
searchSpace Search space for sampling different combinations of models and their hyperparameters. ImageModelDistributionSettingsObjectDetection[]
sweepSettings Model sweeping and hyperparameter sweeping related settings. ImageSweepSettings
validationData Validation data inputs. MLTableJobInput
validationDataSize The fraction of training dataset that needs to be set aside for validation purpose.
Values between (0.0 , 1.0)
Applied when validation dataset is not provided.
 int

Regression

Name Description Value
taskType [Required] Task type for AutoMLJob. "Regression" (required)
cvSplitColumnNames Columns to use for CVSplit data. string[]
featurizationSettings Featurization inputs needed for AutoML job. TableVerticalFeaturizationSettings
fixedParameters Model/training parameters that will remain constant throughout training. TableFixedParameters
limitSettings Execution constraints for AutoMLJob. TableVerticalLimitSettings
nCrossValidations Number of cross validation folds to be applied on training dataset
when validation dataset is not provided.		 NCrossValidations
primaryMetric Primary metric for regression task. "NormalizedMeanAbsoluteError"
"NormalizedRootMeanSquaredError"
"R2Score"
"SpearmanCorrelation"
searchSpace Search space for sampling different combinations of models and their hyperparameters. TableParameterSubspace[]
sweepSettings Settings for model sweeping and hyperparameter tuning. TableSweepSettings
testData Test data input. MLTableJobInput
testDataSize The fraction of test dataset that needs to be set aside for validation purpose.
Values between (0.0 , 1.0)
Applied when validation dataset is not provided.		 int
trainingSettings Inputs for training phase for an AutoML Job. RegressionTrainingSettings
validationData Validation data inputs. MLTableJobInput
validationDataSize The fraction of training dataset that needs to be set aside for validation purpose.
Values between (0.0 , 1.0)
Applied when validation dataset is not provided.		 int
weightColumnName The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down. string

RegressionTrainingSettings

Name Description Value
allowedTrainingAlgorithms Allowed models for regression task. String array containing any of:
"DecisionTree"
"ElasticNet"
"ExtremeRandomTrees"
"GradientBoosting"
"KNN"
"LassoLars"
"LightGBM"
"RandomForest"
"SGD"
"XGBoostRegressor"
blockedTrainingAlgorithms Blocked models for regression task. String array containing any of:
"DecisionTree"
"ElasticNet"
"ExtremeRandomTrees"
"GradientBoosting"
"KNN"
"LassoLars"
"LightGBM"
"RandomForest"
"SGD"
"XGBoostRegressor"
enableDnnTraining Enable recommendation of DNN models. bool
enableModelExplainability Flag to turn on explainability on best model. bool
enableOnnxCompatibleModels Flag for enabling onnx compatible models. bool
enableStackEnsemble Enable stack ensemble run. bool
enableVoteEnsemble Enable voting ensemble run. bool
ensembleModelDownloadTimeout During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded.
Configure this parameter with a higher value than 300 secs, if more time is needed.		 string
stackEnsembleSettings Stack ensemble settings for stack ensemble run. StackEnsembleSettings
trainingMode TrainingMode mode - Setting to 'auto' is same as setting it to 'non-distributed' for now, however in the future may result in mixed mode or heuristics based mode selection. Default is 'auto'.
If 'Distributed' then only distributed featurization is used and distributed algorithms are chosen.
If 'NonDistributed' then only non distributed algorithms are chosen.
 "Auto"
"Distributed"
"NonDistributed"

TextClassification

Name Description Value
taskType [Required] Task type for AutoMLJob. "TextClassification" (required)
featurizationSettings Featurization inputs needed for AutoML job. NlpVerticalFeaturizationSettings
fixedParameters Model/training parameters that will remain constant throughout training. NlpFixedParameters
limitSettings Execution constraints for AutoMLJob. NlpVerticalLimitSettings
primaryMetric Primary metric for Text-Classification task. "AUCWeighted"
"Accuracy"
"AveragePrecisionScoreWeighted"
"NormMacroRecall"
"PrecisionScoreWeighted"
searchSpace Search space for sampling different combinations of models and their hyperparameters. NlpParameterSubspace[]
sweepSettings Settings for model sweeping and hyperparameter tuning. NlpSweepSettings
validationData Validation data inputs. MLTableJobInput

NlpVerticalFeaturizationSettings

Name Description Value
datasetLanguage Dataset language, useful for the text data. string

NlpFixedParameters

Name Description Value
gradientAccumulationSteps Number of steps to accumulate gradients over before running a backward pass. int
learningRate The learning rate for the training procedure. int
learningRateScheduler The type of learning rate schedule to use during the training procedure. "Constant"
"ConstantWithWarmup"
"Cosine"
"CosineWithRestarts"
"Linear"
"None"
"Polynomial"
modelName The name of the model to train. string
numberOfEpochs Number of training epochs. int
trainingBatchSize The batch size for the training procedure. int
validationBatchSize The batch size to be used during evaluation. int
warmupRatio The warmup ratio, used alongside LrSchedulerType. int
weightDecay The weight decay for the training procedure. int

NlpVerticalLimitSettings

Name Description Value
maxConcurrentTrials Maximum Concurrent AutoML iterations. int
maxNodes Maximum nodes to use for the experiment. int
maxTrials Number of AutoML iterations. int
timeout AutoML job timeout. string
trialTimeout Timeout for individual HD trials. string

NlpParameterSubspace

Name Description Value
gradientAccumulationSteps Number of steps to accumulate gradients over before running a backward pass. string
learningRate The learning rate for the training procedure. string
learningRateScheduler The type of learning rate schedule to use during the training procedure. string
modelName The name of the model to train. string
numberOfEpochs Number of training epochs. string
trainingBatchSize The batch size for the training procedure. string
validationBatchSize The batch size to be used during evaluation. string
warmupRatio The warmup ratio, used alongside LrSchedulerType. string
weightDecay The weight decay for the training procedure. string

NlpSweepSettings

Name Description Value
earlyTermination Type of early termination policy for the sweeping job. EarlyTerminationPolicy
samplingAlgorithm [Required] Type of sampling algorithm. "Bayesian"
"Grid"
"Random" (required)

TextClassificationMultilabel

Name Description Value
taskType [Required] Task type for AutoMLJob. "TextClassificationMultilabel" (required)
featurizationSettings Featurization inputs needed for AutoML job. NlpVerticalFeaturizationSettings
fixedParameters Model/training parameters that will remain constant throughout training. NlpFixedParameters
limitSettings Execution constraints for AutoMLJob. NlpVerticalLimitSettings
searchSpace Search space for sampling different combinations of models and their hyperparameters. NlpParameterSubspace[]
sweepSettings Settings for model sweeping and hyperparameter tuning. NlpSweepSettings
validationData Validation data inputs. MLTableJobInput

TextNer

Name Description Value
taskType [Required] Task type for AutoMLJob. "TextNER" (required)
featurizationSettings Featurization inputs needed for AutoML job. NlpVerticalFeaturizationSettings
fixedParameters Model/training parameters that will remain constant throughout training. NlpFixedParameters
limitSettings Execution constraints for AutoMLJob. NlpVerticalLimitSettings
searchSpace Search space for sampling different combinations of models and their hyperparameters. NlpParameterSubspace[]
sweepSettings Settings for model sweeping and hyperparameter tuning. NlpSweepSettings
validationData Validation data inputs. MLTableJobInput

CommandJob

Name Description Value
jobType [Required] Specifies the type of job. "Command" (required)
autologgerSettings Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null. AutologgerSettings
codeId ARM resource ID of the code asset. string
command [Required] The command to execute on startup of the job. eg. "python train.py" string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]
distribution Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, Ray, or null. DistributionConfiguration
environmentId [Required] The ARM resource ID of the Environment specification for the job. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]
environmentVariables Environment variables included in the job. CommandJobEnvironmentVariables
inputs Mapping of input data bindings used in the job. CommandJobInputs
limits Command Job limit. CommandJobLimits
outputs Mapping of output data bindings used in the job. CommandJobOutputs
queueSettings Queue settings for the job QueueSettings
resources Compute Resource configuration for the job. JobResourceConfiguration

AutologgerSettings

Name Description Value
mlflowAutologger [Required] Indicates whether mlflow autologger is enabled. "Disabled"
"Enabled" (required)

DistributionConfiguration

Name Description Value
distributionType Set the object type Mpi
PyTorch
Ray
TensorFlow (required)

Mpi

Name Description Value
distributionType [Required] Specifies the type of distribution framework. "Mpi" (required)
processCountPerInstance Number of processes per MPI node. int

PyTorch

Name Description Value
distributionType [Required] Specifies the type of distribution framework. "PyTorch" (required)
processCountPerInstance Number of processes per node. int

Ray

Name Description Value
distributionType [Required] Specifies the type of distribution framework. "Ray" (required)
address The address of Ray head node. string
dashboardPort The port to bind the dashboard server to. int
headNodeAdditionalArgs Additional arguments passed to ray start in head node. string
includeDashboard Provide this argument to start the Ray dashboard GUI. bool
port The port of the head ray process. int
workerNodeAdditionalArgs Additional arguments passed to ray start in worker node. string

TensorFlow

Name Description Value
distributionType [Required] Specifies the type of distribution framework. "TensorFlow" (required)
parameterServerCount Number of parameter server tasks. int
workerCount Number of workers. If not specified, will default to the instance count. int

CommandJobEnvironmentVariables

Name Description Value
{customized property} string

CommandJobInputs

Name Description Value
{customized property} JobInput

JobInput

Name Description Value
description Description for the input. string
jobInputType Set the object type custom_model
literal
mlflow_model
mltable
triton_model
uri_file
uri_folder (required)

CustomModelJobInput

Name Description Value
jobInputType [Required] Specifies the type of job. "custom_model" (required)
mode Input Asset Delivery Mode. "Direct"
"Download"
"EvalDownload"
"EvalMount"
"ReadOnlyMount"
"ReadWriteMount"
uri [Required] Input Asset URI. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]

LiteralJobInput

Name Description Value
jobInputType [Required] Specifies the type of job. "literal" (required)
value [Required] Literal value for the input. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]

TritonModelJobInput

Name Description Value
jobInputType [Required] Specifies the type of job. "triton_model" (required)
mode Input Asset Delivery Mode. "Direct"
"Download"
"EvalDownload"
"EvalMount"
"ReadOnlyMount"
"ReadWriteMount"
uri [Required] Input Asset URI. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]

UriFileJobInput

Name Description Value
jobInputType [Required] Specifies the type of job. "uri_file" (required)
mode Input Asset Delivery Mode. "Direct"
"Download"
"EvalDownload"
"EvalMount"
"ReadOnlyMount"
"ReadWriteMount"
uri [Required] Input Asset URI. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]

UriFolderJobInput

Name Description Value
jobInputType [Required] Specifies the type of job. "uri_folder" (required)
mode Input Asset Delivery Mode. "Direct"
"Download"
"EvalDownload"
"EvalMount"
"ReadOnlyMount"
"ReadWriteMount"
uri [Required] Input Asset URI. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]

CommandJobLimits

Name Description Value
jobLimitsType [Required] JobLimit type. "Command"
"Sweep" (required)
timeout The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds. string

CommandJobOutputs

Name Description Value
{customized property} JobOutput

LabelingJobProperties

Name Description Value
componentId ARM resource ID of the component resource. string
computeId ARM resource ID of the compute resource. string
dataConfiguration Configuration of data used in the job. LabelingDataConfiguration
description The asset description text. string
displayName Display name of job. string
experimentName The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment. string
identity Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null.
Defaults to AmlToken if null.		 IdentityConfiguration
isArchived Is the asset archived? bool
jobInstructions Labeling instructions of the job. LabelingJobInstructions
jobType [Required] Specifies the type of job. "AutoML"
"Command"
"Labeling"
"Pipeline"
"Spark"
"Sweep" (required)
labelCategories Label categories of the job. LabelingJobLabelCategories
labelingJobMediaProperties Media type specific properties in the job. LabelingJobMediaProperties
mlAssistConfiguration Configuration of MLAssist feature in the job. MLAssistConfiguration
notificationSetting Notification setting for the job NotificationSetting
properties The asset property dictionary. ResourceBaseProperties
secretsConfiguration Configuration for secrets to be made available during runtime. JobBaseSecretsConfiguration
services List of JobEndpoints.
For local jobs, a job endpoint will have an endpoint value of FileStreamObject.		 JobBaseServices
tags Tag dictionary. Tags can be added, removed, and updated. object

LabelingDataConfiguration

Name Description Value
dataId Resource Id of the data asset to perform labeling. string
incrementalDataRefresh Indicates whether to enable incremental data refresh. "Disabled"
"Enabled"

LabelingJobInstructions

Name Description Value
uri The link to a page with detailed labeling instructions for labelers. string

LabelingJobLabelCategories

Name Description Value
{customized property} LabelCategory

LabelCategory

Name Description Value
classes Dictionary of label classes in this category. LabelCategoryClasses
displayName Display name of the label category. string
multiSelect Indicates whether it is allowed to select multiple classes in this category. "Disabled"
"Enabled"

LabelCategoryClasses

Name Description Value
{customized property} LabelClass

LabelClass

Name Description Value
displayName Display name of the label class. string
subclasses Dictionary of subclasses of the label class. LabelClassSubclasses

LabelClassSubclasses

Name Description Value
{customized property} LabelClass

LabelingJobMediaProperties

Name Description Value
mediaType Set the object type Image
Text (required)

LabelingJobImageProperties

Name Description Value
mediaType [Required] Media type of the job. "Image" (required)
annotationType Annotation type of image labeling job. "BoundingBox"
"Classification"
"InstanceSegmentation"

LabelingJobTextProperties

Name Description Value
mediaType [Required] Media type of the job. "Text" (required)
annotationType Annotation type of text labeling job. "Classification"
"NamedEntityRecognition"

MLAssistConfiguration

Name Description Value
mlAssist Set the object type Disabled
Enabled (required)

MLAssistConfigurationDisabled

Name Description Value
mlAssist [Required] Indicates whether MLAssist feature is enabled. "Disabled" (required)

MLAssistConfigurationEnabled

Name Description Value
mlAssist [Required] Indicates whether MLAssist feature is enabled. "Enabled" (required)
inferencingComputeBinding [Required] AML compute binding used in inferencing. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]
trainingComputeBinding [Required] AML compute binding used in training. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]

PipelineJob

Name Description Value
jobType [Required] Specifies the type of job. "Pipeline" (required)
inputs Inputs for the pipeline job. PipelineJobInputs
jobs Jobs construct the Pipeline Job. PipelineJobJobs
outputs Outputs for the pipeline job PipelineJobOutputs
settings Pipeline settings, for things like ContinueRunOnStepFailure etc.
sourceJobId ARM resource ID of source job. string

PipelineJobInputs

Name Description Value
{customized property} JobInput

PipelineJobJobs

Name Description Value
{customized property}

PipelineJobOutputs

Name Description Value
{customized property} JobOutput

SparkJob

Name Description Value
jobType [Required] Specifies the type of job. "Spark" (required)
archives Archive files used in the job. string[]
args Arguments for the job. string
codeId [Required] ARM resource ID of the code asset. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]
conf Spark configured properties. SparkJobConf
entry [Required] The entry to execute on startup of the job. SparkJobEntry (required)
environmentId The ARM resource ID of the Environment specification for the job. string
files Files used in the job. string[]
inputs Mapping of input data bindings used in the job. SparkJobInputs
jars Jar files used in the job. string[]
outputs Mapping of output data bindings used in the job. SparkJobOutputs
pyFiles Python files used in the job. string[]
queueSettings Queue settings for the job QueueSettings
resources Compute Resource configuration for the job. SparkResourceConfiguration

SparkJobConf

Name Description Value
{customized property} string

SparkJobEntry

Name Description Value
sparkJobEntryType Set the object type SparkJobPythonEntry
SparkJobScalaEntry (required)

SparkJobPythonEntry

Name Description Value
sparkJobEntryType [Required] Type of the job's entry point. "SparkJobPythonEntry" (required)
file [Required] Relative python file path for job entry point. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]

SparkJobScalaEntry

Name Description Value
sparkJobEntryType [Required] Type of the job's entry point. "SparkJobScalaEntry" (required)
className [Required] Scala class name used as entry point. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]

SparkJobInputs

Name Description Value
{customized property} JobInput

SparkJobOutputs

Name Description Value
{customized property} JobOutput

SparkResourceConfiguration

Name Description Value
instanceType Optional type of VM used as supported by the compute target. string
runtimeVersion Version of spark runtime used for the job. string

SweepJob

Name Description Value
jobType [Required] Specifies the type of job. "Sweep" (required)
earlyTermination Early termination policies enable canceling poor-performing runs before they complete EarlyTerminationPolicy
inputs Mapping of input data bindings used in the job. SweepJobInputs
limits Sweep Job limit. SweepJobLimits
objective [Required] Optimization objective. Objective (required)
outputs Mapping of output data bindings used in the job. SweepJobOutputs
queueSettings Queue settings for the job QueueSettings
samplingAlgorithm [Required] The hyperparameter sampling algorithm SamplingAlgorithm (required)
searchSpace [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
trial [Required] Trial component definition. TrialComponent (required)

SweepJobInputs

Name Description Value
{customized property} JobInput

SweepJobLimits

Name Description Value
jobLimitsType [Required] JobLimit type. "Command"
"Sweep" (required)
maxConcurrentTrials Sweep Job max concurrent trials. int
maxTotalTrials Sweep Job max total trials. int
timeout The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds. string
trialTimeout Sweep Job Trial timeout value. string

Objective

Name Description Value
goal [Required] Defines supported metric goals for hyperparameter tuning "Maximize"
"Minimize" (required)
primaryMetric [Required] Name of the metric to optimize. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]

SweepJobOutputs

Name Description Value
{customized property} JobOutput

SamplingAlgorithm

Name Description Value
samplingAlgorithmType Set the object type Bayesian
Grid
Random (required)

BayesianSamplingAlgorithm

Name Description Value
samplingAlgorithmType [Required] The algorithm used for generating hyperparameter values, along with configuration properties "Bayesian" (required)

GridSamplingAlgorithm

Name Description Value
samplingAlgorithmType [Required] The algorithm used for generating hyperparameter values, along with configuration properties "Grid" (required)

RandomSamplingAlgorithm

Name Description Value
samplingAlgorithmType [Required] The algorithm used for generating hyperparameter values, along with configuration properties "Random" (required)
logbase An optional positive number or e in string format to be used as base for log based random sampling string
rule The specific type of random algorithm "Random"
"Sobol"
seed An optional integer to use as the seed for random number generation int

TrialComponent

Name Description Value
codeId ARM resource ID of the code asset. string
command [Required] The command to execute on startup of the job. eg. "python train.py" string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]
distribution Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null. DistributionConfiguration
environmentId [Required] The ARM resource ID of the Environment specification for the job. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]
environmentVariables Environment variables included in the job. TrialComponentEnvironmentVariables
resources Compute Resource configuration for the job. JobResourceConfiguration

TrialComponentEnvironmentVariables

Name Description Value
{customized property} string

CreateMonitorAction

Name Description Value
actionType [Required] Specifies the action type of the schedule "CreateMonitor" (required)
monitorDefinition [Required] Defines the monitor. MonitorDefinition (required)

MonitorDefinition

Name Description Value
alertNotificationSetting The monitor's notification settings. MonitoringAlertNotificationSettingsBase
computeId [Required] The ARM resource ID of the compute resource to run the monitoring job on. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]
monitoringTarget The ARM resource ID of either the model or deployment targeted by this monitor. string
signals [Required] The signals to monitor. MonitorDefinitionSignals (required)

MonitoringAlertNotificationSettingsBase

Name Description Value
alertNotificationType Set the object type AzureMonitor
Email (required)

AzMonMonitoringAlertNotificationSettings

Name Description Value
alertNotificationType [Required] Specifies the type of signal to monitor. "AzureMonitor" (required)

EmailMonitoringAlertNotificationSettings

Name Description Value
alertNotificationType [Required] Specifies the type of signal to monitor. "Email" (required)
emailNotificationSetting Configuration for notification. NotificationSetting

MonitorDefinitionSignals

Name Description Value
{customized property} MonitoringSignalBase

MonitoringSignalBase

Name Description Value
lookbackPeriod The amount of time a single monitor should look back over the target data on a given run. string
mode The current notification mode for this signal. "Disabled"
"Enabled"
signalType Set the object type Custom
DataDrift
DataQuality
FeatureAttributionDrift
ModelPerformance
PredictionDrift (required)

CustomMonitoringSignal

Name Description Value
signalType [Required] Specifies the type of signal to monitor. "Custom" (required)
componentId [Required] ARM resource ID of the component resource used to calculate the custom metrics. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]
inputAssets Monitoring assets to take as input. Key is the component input port name, value is the data asset. CustomMonitoringSignalInputAssets
metricThresholds [Required] A list of metrics to calculate and their associated thresholds. CustomMetricThreshold[] (required)

CustomMonitoringSignalInputAssets

Name Description Value
{customized property} MonitoringInputData

MonitoringInputData

Name Description Value
asset The data asset input to be leveraged by the monitoring job..
dataContext [Required] The context of the data source. "GroundTruth"
"ModelInputs"
"ModelOutputs"
"Test"
"Training"
"Validation" (required)
preprocessingComponentId The ARM resource ID of the component resource used to preprocess the data. string
targetColumnName The target column in the given data asset to leverage. string

CustomMetricThreshold

Name Description Value
metric [Required] The user-defined metric to calculate. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]
threshold The threshold value. If null, a default value will be set depending on the selected metric. MonitoringThreshold

MonitoringThreshold

Name Description Value
value The threshold value. If null, the set default is dependent on the metric type. int

DataDriftMonitoringSignal

Name Description Value
signalType [Required] Specifies the type of signal to monitor. "DataDrift" (required)
baselineData [Required] The data to calculate drift against. MonitoringInputData (required)
dataSegment The data segment used for scoping on a subset of the data population. MonitoringDataSegment
features The feature filter which identifies which feature to calculate drift over. MonitoringFeatureFilterBase
metricThresholds [Required] A list of metrics to calculate and their associated thresholds. DataDriftMetricThresholdBase[] (required)
targetData [Required] The data which drift will be calculated for. MonitoringInputData (required)

MonitoringDataSegment

Name Description Value
feature The feature to segment the data on. string
values Filters for only the specified values of the given segmented feature. string[]

MonitoringFeatureFilterBase

Name Description Value
filterType Set the object type AllFeatures
FeatureSubset
TopNByAttribution (required)

AllFeatures

Name Description Value
filterType [Required] Specifies the feature filter to leverage when selecting features to calculate metrics over. "AllFeatures" (required)

FeatureSubset

Name Description Value
filterType [Required] Specifies the feature filter to leverage when selecting features to calculate metrics over. "FeatureSubset" (required)
features [Required] The list of features to include. string[] (required)

TopNFeaturesByAttribution

Name Description Value
filterType [Required] Specifies the feature filter to leverage when selecting features to calculate metrics over. "TopNByAttribution" (required)
top The number of top features to include. int

DataDriftMetricThresholdBase

Name Description Value
threshold The threshold value. If null, a default value will be set depending on the selected metric. MonitoringThreshold
dataType Set the object type Categorical
Numerical (required)

CategoricalDataDriftMetricThreshold

Name Description Value
dataType [Required] Specifies the data type of the metric threshold. "Categorical" (required)
metric [Required] The categorical data drift metric to calculate. "JensenShannonDistance"
"PearsonsChiSquaredTest"
"PopulationStabilityIndex" (required)

NumericalDataDriftMetricThreshold

Name Description Value
dataType [Required] Specifies the data type of the metric threshold. "Numerical" (required)
metric [Required] The numerical data drift metric to calculate. "JensenShannonDistance"
"NormalizedWassersteinDistance"
"PopulationStabilityIndex"
"TwoSampleKolmogorovSmirnovTest" (required)

DataQualityMonitoringSignal

Name Description Value
signalType [Required] Specifies the type of signal to monitor. "DataQuality" (required)
baselineData [Required] The data to calculate drift against. MonitoringInputData (required)
features The features to calculate drift over. MonitoringFeatureFilterBase
metricThresholds [Required] A list of metrics to calculate and their associated thresholds. DataQualityMetricThresholdBase[] (required)
targetData [Required] The data produced by the production service which drift will be calculated for. MonitoringInputData (required)

DataQualityMetricThresholdBase

Name Description Value
threshold The threshold value. If null, a default value will be set depending on the selected metric. MonitoringThreshold
dataType Set the object type Categorical
Numerical (required)

CategoricalDataQualityMetricThreshold

Name Description Value
dataType [Required] Specifies the data type of the metric threshold. "Categorical" (required)
metric [Required] The categorical data quality metric to calculate. "DataTypeErrorRate"
"NullValueRate"
"OutOfBoundsRate" (required)

NumericalDataQualityMetricThreshold

Name Description Value
dataType [Required] Specifies the data type of the metric threshold. "Numerical" (required)
metric [Required] The numerical data quality metric to calculate. "DataTypeErrorRate"
"NullValueRate"
"OutOfBoundsRate" (required)

FeatureAttributionDriftMonitoringSignal

Name Description Value
signalType [Required] Specifies the type of signal to monitor. "FeatureAttributionDrift" (required)
baselineData [Required] The data to calculate drift against. MonitoringInputData (required)
metricThreshold [Required] A list of metrics to calculate and their associated thresholds. FeatureAttributionMetricThreshold (required)
modelType [Required] The type of task the model performs. "Classification"
"Regression" (required)
targetData [Required] The data which drift will be calculated for. MonitoringInputData (required)

FeatureAttributionMetricThreshold

Name Description Value
metric [Required] The feature attribution metric to calculate. "NormalizedDiscountedCumulativeGain" (required)
threshold The threshold value. If null, a default value will be set depending on the selected metric. MonitoringThreshold

ModelPerformanceSignalBase

Name Description Value
signalType [Required] Specifies the type of signal to monitor. "ModelPerformance" (required)
baselineData [Required] The data to calculate drift against. MonitoringInputData (required)
dataSegment The data segment. MonitoringDataSegment
metricThreshold [Required] A list of metrics to calculate and their associated thresholds. ModelPerformanceMetricThresholdBase (required)
targetData [Required] The data produced by the production service which drift will be calculated for. MonitoringInputData (required)

ModelPerformanceMetricThresholdBase

Name Description Value
threshold The threshold value. If null, a default value will be set depending on the selected metric. MonitoringThreshold
modelType Set the object type Classification
Regression (required)

ClassificationModelPerformanceMetricThreshold

Name Description Value
modelType [Required] Specifies the data type of the metric threshold. "Classification" (required)
metric [Required] The classification model performance to calculate. "Accuracy"
"F1Score"
"Precision"
"Recall" (required)

RegressionModelPerformanceMetricThreshold

Name Description Value
modelType [Required] Specifies the data type of the metric threshold. "Regression" (required)
metric [Required] The regression model performance metric to calculate. "MeanAbsoluteError"
"MeanSquaredError"
"RootMeanSquaredError" (required)

PredictionDriftMonitoringSignal

Name Description Value
signalType [Required] Specifies the type of signal to monitor. "PredictionDrift" (required)
baselineData [Required] The data to calculate drift against. MonitoringInputData (required)
metricThresholds [Required] A list of metrics to calculate and their associated thresholds. PredictionDriftMetricThresholdBase[] (required)
modelType [Required] The type of the model monitored. "Classification"
"Regression" (required)
targetData [Required] The data which drift will be calculated for. MonitoringInputData (required)

PredictionDriftMetricThresholdBase

Name Description Value
threshold The threshold value. If null, a default value will be set depending on the selected metric. MonitoringThreshold
dataType Set the object type Categorical
Numerical (required)

CategoricalPredictionDriftMetricThreshold

Name Description Value
dataType [Required] Specifies the data type of the metric threshold. "Categorical" (required)
metric [Required] The categorical prediction drift metric to calculate. "JensenShannonDistance"
"PearsonsChiSquaredTest"
"PopulationStabilityIndex" (required)

NumericalPredictionDriftMetricThreshold

Name Description Value
dataType [Required] Specifies the data type of the metric threshold. "Numerical" (required)
metric [Required] The numerical prediction drift metric to calculate. "JensenShannonDistance"
"NormalizedWassersteinDistance"
"PopulationStabilityIndex"
"TwoSampleKolmogorovSmirnovTest" (required)

ImportDataAction

Name Description Value
actionType [Required] Specifies the action type of the schedule "ImportData" (required)
dataImportDefinition [Required] Defines Schedule action definition details. DataImport (required)

DataImport

Name Description Value
assetName Name of the asset for data import job to create string
autoDeleteSetting Specifies the lifecycle setting of managed data asset. AutoDeleteSetting
dataType [Required] Specifies the type of data. "mltable"
"uri_file"
"uri_folder" (required)
dataUri [Required] Uri of the data. Example: https://go.microsoft.com/fwlink/?linkid=2202330 string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]
description The asset description text. string
intellectualProperty Intellectual Property details. Used if data is an Intellectual Property. IntellectualProperty
isAnonymous If the name version are system generated (anonymous registration). For types where Stage is defined, when Stage is provided it will be used to populate IsAnonymous bool
isArchived Is the asset archived? For types where Stage is defined, when Stage is provided it will be used to populate IsArchived bool
properties The asset property dictionary. ResourceBaseProperties
source Source data of the asset to import from DataImportSource
stage Stage in the data lifecycle assigned to this data asset string
tags Tag dictionary. Tags can be added, removed, and updated. object

IntellectualProperty

Name Description Value
protectionLevel Protection level of the Intellectual Property. "All"
"None"
publisher [Required] Publisher of the Intellectual Property. Must be the same as Registry publisher name. string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]

DataImportSource

Name Description Value
connection Workspace connection for data import source storage string
sourceType Set the object type database
file_system (required)

DatabaseSource

Name Description Value
sourceType [Required] Specifies the type of data. "database" (required)
query SQL Query statement for data import Database source string
storedProcedure SQL StoredProcedure on data import Database source string
storedProcedureParams SQL StoredProcedure parameters DatabaseSourceStoredProcedureParamsItem[]
tableName Name of the table on data import Database source string

DatabaseSourceStoredProcedureParamsItem

Name Description Value
{customized property} string

FileSystemSource

Name Description Value
sourceType [Required] Specifies the type of data. "file_system" (required)
path Path on data import FileSystem source string

EndpointScheduleAction

Name Description Value
actionType [Required] Specifies the action type of the schedule "InvokeBatchEndpoint" (required)
endpointInvocationDefinition [Required] Defines Schedule action definition details.
{see href="TBD" /}

TriggerBase

Name Description Value
endTime Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer https://en.wikipedia.org/wiki/ISO_8601.
Recommented format would be "2022-06-01T00:00:01"
If not present, the schedule will run indefinitely		 string
startTime Specifies start time of schedule in ISO 8601 format, but without a UTC offset. string
timeZone Specifies time zone in which the schedule runs.
TimeZone should follow Windows time zone format. Refer: /windows-hardware/manufacture/desktop/default-time-zones />		 string
triggerType Set the object type Cron
Recurrence (required)

CronTrigger

Name Description Value
triggerType [Required] "Cron" (required)
expression [Required] Specifies cron expression of schedule.
The expression should follow NCronTab format.		 string (required)

Constraints:
Min length = 1
Pattern = [a-zA-Z0-9_]

RecurrenceTrigger

Name Description Value
endTime Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer https://en.wikipedia.org/wiki/ISO_8601.
Recommented format would be "2022-06-01T00:00:01"
If not present, the schedule will run indefinitely		 string
frequency [Required] The frequency to trigger schedule. "Day"
"Hour"
"Minute"
"Month"
"Week" (required)
interval [Required] Specifies schedule interval in conjunction with frequency int (required)
schedule The recurrence schedule. RecurrenceSchedule
startTime Specifies start time of schedule in ISO 8601 format, but without a UTC offset. string
timeZone Specifies time zone in which the schedule runs.
TimeZone should follow Windows time zone format. Refer: /windows-hardware/manufacture/desktop/default-time-zones		 string
triggerType [Required] "Cron"
"Recurrence" (required)

RecurrenceSchedule

Name Description Value
hours [Required] List of hours for the schedule. int[] (required)
minutes [Required] List of minutes for the schedule. int[] (required)
monthDays List of month days for the schedule int[]
weekDays List of days for the schedule. String array containing any of:
"Friday"
"Monday"
"Saturday"
"Sunday"
"Thursday"
"Tuesday"
"Wednesday"