Jobs - Create Or Update

Service:

Machine Learning

API Version:

2023-10-01

Creates and executes a Job. For update case, the Tags in the definition passed in will replace Tags in the existing job.

PUT https://management.azure.com/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/jobs/{id}?api-version=2023-10-01

URI Parameters

Name	In	Required	Type	Description
id	path	True	string	The name and identifier for the Job. This is case-sensitive. Regex pattern: ^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$
resourceGroupName	path	True	string	The name of the resource group. The name is case insensitive.
subscriptionId	path	True	string	The ID of the target subscription.
workspaceName	path	True	string	Name of Azure Machine Learning workspace. Regex pattern: ^[a-zA-Z0-9][a-zA-Z0-9_-]{2,32}$
api-version	query	True	string	The API version to use for this operation.

Request Body

Name	Required	Type	Description
properties	True	JobBase: AutoMLJob CommandJob PipelineJob SweepJob	[Required] Additional attributes of the entity.

Responses

Name	Type	Description
200 OK	JobBaseResource	Create or update request is successful.
201 Created	JobBaseResource	Created
Other Status Codes	ErrorResponse	Error

Examples

CreateOrUpdate AutoML Job.

CreateOrUpdate Command Job.

CreateOrUpdate Pipeline Job.

CreateOrUpdate Sweep Job.

CreateOrUpdate AutoML Job.

Sample request

HTTP

PUT https://management.azure.com/subscriptions/00000000-1111-2222-3333-444444444444/resourceGroups/test-rg/providers/Microsoft.MachineLearningServices/workspaces/my-aml-workspace/jobs/string?api-version=2023-10-01

{
  "properties": {
    "description": "string",
    "tags": {
      "string": "string"
    },
    "properties": {
      "string": "string"
    },
    "displayName": "string",
    "experimentName": "string",
    "services": {
      "string": {
        "jobServiceType": "string",
        "port": 1,
        "endpoint": "string",
        "properties": {
          "string": "string"
        }
      }
    },
    "computeId": "string",
    "isArchived": false,
    "identity": {
      "identityType": "AMLToken"
    },
    "jobType": "AutoML",
    "resources": {
      "instanceCount": 1,
      "instanceType": "string",
      "properties": {
        "string": {
          "9bec0ab0-c62f-4fa9-a97c-7b24bbcc90ad": null
        }
      }
    },
    "environmentId": "string",
    "environmentVariables": {
      "string": "string"
    },
    "taskDetails": {
      "taskType": "ImageClassification",
      "limitSettings": {
        "maxTrials": 2
      },
      "targetColumnName": "string",
      "trainingData": {
        "jobInputType": "mltable",
        "uri": "string"
      },
      "modelSettings": {
        "validationCropSize": 2
      },
      "searchSpace": [
        {
          "validationCropSize": "choice(2, 360)"
        }
      ]
    },
    "outputs": {
      "string": {
        "description": "string",
        "uri": "string",
        "mode": "ReadWriteMount",
        "jobOutputType": "uri_file"
      }
    }
  }
}

Sample response

Status code:

200

{
  "id": "string",
  "name": "string",
  "type": "string",
  "properties": {
    "description": "string",
    "tags": {
      "string": "string"
    },
    "properties": {
      "string": "string"
    },
    "displayName": "string",
    "status": "Scheduled",
    "experimentName": "string",
    "services": {
      "string": {
        "jobServiceType": "string",
        "port": 1,
        "endpoint": "string",
        "status": "string",
        "errorMessage": "string",
        "properties": {
          "string": "string"
        }
      }
    },
    "computeId": "string",
    "isArchived": false,
    "identity": {
      "identityType": "AMLToken"
    },
    "jobType": "AutoML",
    "resources": {
      "instanceCount": 1,
      "instanceType": "string",
      "properties": {
        "string": {
          "9bec0ab0-c62f-4fa9-a97c-7b24bbcc90ad": null
        }
      }
    },
    "environmentId": "string",
    "environmentVariables": {
      "string": "string"
    },
    "taskDetails": {
      "taskType": "ImageClassification",
      "limitSettings": {
        "maxTrials": 2
      },
      "targetColumnName": "string",
      "trainingData": {
        "jobInputType": "mltable",
        "uri": "string"
      },
      "modelSettings": {
        "validationCropSize": 2
      },
      "searchSpace": [
        {
          "validationCropSize": "choice(2, 360)"
        }
      ]
    },
    "outputs": {
      "string": {
        "description": "string",
        "uri": "string",
        "mode": "ReadWriteMount",
        "jobOutputType": "uri_file"
      }
    }
  },
  "systemData": {
    "createdAt": "2020-01-01T12:34:56.999Z",
    "createdBy": "string",
    "createdByType": "User",
    "lastModifiedAt": "2020-01-01T12:34:56.999Z",
    "lastModifiedBy": "string",
    "lastModifiedByType": "ManagedIdentity"
  }
}

Status code:

201

{
  "id": "string",
  "name": "string",
  "type": "string",
  "properties": {
    "description": "string",
    "tags": {
      "string": "string"
    },
    "properties": {
      "string": "string"
    },
    "displayName": "string",
    "status": "Scheduled",
    "experimentName": "string",
    "services": {
      "string": {
        "jobServiceType": "string",
        "port": 1,
        "endpoint": "string",
        "status": "string",
        "errorMessage": "string",
        "properties": {
          "string": "string"
        }
      }
    },
    "computeId": "string",
    "isArchived": false,
    "identity": {
      "identityType": "AMLToken"
    },
    "jobType": "AutoML",
    "resources": {
      "instanceCount": 1,
      "instanceType": "string",
      "properties": {
        "string": {
          "9bec0ab0-c62f-4fa9-a97c-7b24bbcc90ad": null
        }
      }
    },
    "environmentId": "string",
    "environmentVariables": {
      "string": "string"
    },
    "taskDetails": {
      "taskType": "ImageClassification",
      "limitSettings": {
        "maxTrials": 2
      },
      "targetColumnName": "string",
      "trainingData": {
        "jobInputType": "mltable",
        "uri": "string"
      },
      "modelSettings": {
        "validationCropSize": 2
      },
      "searchSpace": [
        {
          "validationCropSize": "choice(2, 360)"
        }
      ]
    },
    "outputs": {
      "string": {
        "description": "string",
        "uri": "string",
        "mode": "ReadWriteMount",
        "jobOutputType": "uri_file"
      }
    }
  },
  "systemData": {
    "createdAt": "2020-01-01T12:34:56.999Z",
    "createdBy": "string",
    "createdByType": "User",
    "lastModifiedAt": "2020-01-01T12:34:56.999Z",
    "lastModifiedBy": "string",
    "lastModifiedByType": "ManagedIdentity"
  }
}

CreateOrUpdate Command Job.

Sample request

HTTP

PUT https://management.azure.com/subscriptions/00000000-1111-2222-3333-444444444444/resourceGroups/test-rg/providers/Microsoft.MachineLearningServices/workspaces/my-aml-workspace/jobs/string?api-version=2023-10-01

{
  "properties": {
    "description": "string",
    "tags": {
      "string": "string"
    },
    "properties": {
      "string": "string"
    },
    "displayName": "string",
    "experimentName": "string",
    "services": {
      "string": {
        "jobServiceType": "string",
        "port": 1,
        "endpoint": "string",
        "properties": {
          "string": "string"
        }
      }
    },
    "computeId": "string",
    "jobType": "Command",
    "resources": {
      "instanceCount": 1,
      "instanceType": "string",
      "properties": {
        "string": {
          "e6b6493e-7d5e-4db3-be1e-306ec641327e": null
        }
      }
    },
    "codeId": "string",
    "command": "string",
    "environmentId": "string",
    "inputs": {
      "string": {
        "description": "string",
        "jobInputType": "literal",
        "value": "string"
      }
    },
    "outputs": {
      "string": {
        "description": "string",
        "jobOutputType": "uri_file",
        "mode": "ReadWriteMount",
        "uri": "string"
      }
    },
    "distribution": {
      "distributionType": "TensorFlow",
      "workerCount": 1,
      "parameterServerCount": 1
    },
    "limits": {
      "timeout": "PT5M",
      "jobLimitsType": "Command"
    },
    "environmentVariables": {
      "string": "string"
    },
    "identity": {
      "identityType": "AMLToken"
    }
  }
}

Sample response

Status code:

200

{
  "id": "string",
  "name": "string",
  "type": "string",
  "properties": {
    "description": "string",
    "tags": {
      "string": "string"
    },
    "properties": {
      "string": "string"
    },
    "displayName": "string",
    "status": "NotStarted",
    "experimentName": "string",
    "services": {
      "string": {
        "jobServiceType": "string",
        "port": 1,
        "endpoint": "string",
        "status": "string",
        "errorMessage": "string",
        "properties": {
          "string": "string"
        }
      }
    },
    "computeId": "string",
    "jobType": "Command",
    "resources": {
      "instanceCount": 1,
      "instanceType": "string",
      "properties": {
        "string": {
          "a0847709-f5aa-4561-8ba5-d915d403fdcf": null
        }
      }
    },
    "codeId": "string",
    "command": "string",
    "environmentId": "string",
    "inputs": {
      "string": {
        "description": "string",
        "jobInputType": "literal",
        "value": "string"
      }
    },
    "outputs": {
      "string": {
        "description": "string",
        "jobOutputType": "uri_file",
        "mode": "ReadWriteMount",
        "uri": "string"
      }
    },
    "distribution": {
      "distributionType": "TensorFlow",
      "workerCount": 1,
      "parameterServerCount": 1
    },
    "limits": {
      "timeout": "PT5M",
      "jobLimitsType": "Command"
    },
    "environmentVariables": {
      "string": "string"
    },
    "identity": {
      "identityType": "AMLToken"
    },
    "parameters": {
      "string": "string"
    }
  },
  "systemData": {
    "createdAt": "2020-01-01T12:34:56.999Z",
    "createdBy": "string",
    "createdByType": "User",
    "lastModifiedAt": "2020-01-01T12:34:56.999Z",
    "lastModifiedBy": "string",
    "lastModifiedByType": "User"
  }
}

Status code:

201

{
  "id": "string",
  "name": "string",
  "type": "string",
  "properties": {
    "description": "string",
    "tags": {
      "string": "string"
    },
    "properties": {
      "string": "string"
    },
    "displayName": "string",
    "status": "NotStarted",
    "experimentName": "string",
    "services": {
      "string": {
        "jobServiceType": "string",
        "port": 1,
        "endpoint": "string",
        "status": "string",
        "errorMessage": "string",
        "properties": {
          "string": "string"
        }
      }
    },
    "computeId": "string",
    "jobType": "Command",
    "resources": {
      "instanceCount": 1,
      "instanceType": "string",
      "properties": {
        "string": {
          "b8163d40-c351-43d6-8a34-d0cd895b8a5a": null
        }
      }
    },
    "codeId": "string",
    "command": "string",
    "environmentId": "string",
    "inputs": {
      "string": {
        "description": "string",
        "jobInputType": "literal",
        "value": "string"
      }
    },
    "outputs": {
      "string": {
        "description": "string",
        "jobOutputType": "uri_file",
        "mode": "ReadWriteMount",
        "uri": "string"
      }
    },
    "distribution": {
      "distributionType": "TensorFlow",
      "workerCount": 1,
      "parameterServerCount": 1
    },
    "limits": {
      "timeout": "PT5M",
      "jobLimitsType": "Command"
    },
    "environmentVariables": {
      "string": "string"
    },
    "identity": {
      "identityType": "AMLToken"
    },
    "parameters": {
      "string": "string"
    }
  },
  "systemData": {
    "createdAt": "2020-01-01T12:34:56.999Z",
    "createdBy": "string",
    "createdByType": "User",
    "lastModifiedAt": "2020-01-01T12:34:56.999Z",
    "lastModifiedBy": "string",
    "lastModifiedByType": "User"
  }
}

CreateOrUpdate Pipeline Job.

Sample request

HTTP

PUT https://management.azure.com/subscriptions/00000000-1111-2222-3333-444444444444/resourceGroups/test-rg/providers/Microsoft.MachineLearningServices/workspaces/my-aml-workspace/jobs/string?api-version=2023-10-01

{
  "properties": {
    "description": "string",
    "tags": {
      "string": "string"
    },
    "properties": {
      "string": "string"
    },
    "displayName": "string",
    "experimentName": "string",
    "services": {
      "string": {
        "jobServiceType": "string",
        "port": 1,
        "endpoint": "string",
        "properties": {
          "string": "string"
        }
      }
    },
    "computeId": "string",
    "jobType": "Pipeline",
    "settings": {},
    "inputs": {
      "string": {
        "description": "string",
        "jobInputType": "literal",
        "value": "string"
      }
    },
    "outputs": {
      "string": {
        "description": "string",
        "jobOutputType": "uri_file",
        "mode": "Upload",
        "uri": "string"
      }
    }
  }
}

Sample response

Status code:

200

{
  "id": "string",
  "name": "string",
  "type": "string",
  "properties": {
    "description": "string",
    "tags": {
      "string": "string"
    },
    "properties": {
      "string": "string"
    },
    "displayName": "string",
    "status": "NotStarted",
    "experimentName": "string",
    "services": {
      "string": {
        "jobServiceType": "string",
        "port": 1,
        "endpoint": "string",
        "status": "string",
        "errorMessage": "string",
        "properties": {
          "string": "string"
        }
      }
    },
    "computeId": "string",
    "jobType": "Pipeline",
    "settings": {},
    "inputs": {
      "string": {
        "description": "string",
        "jobInputType": "literal",
        "value": "string"
      }
    },
    "outputs": {
      "string": {
        "description": "string",
        "jobOutputType": "uri_file",
        "mode": "Upload",
        "uri": "string"
      }
    }
  },
  "systemData": {
    "createdAt": "2020-01-01T12:34:56.999Z",
    "createdBy": "string",
    "createdByType": "User",
    "lastModifiedAt": "2020-01-01T12:34:56.999Z",
    "lastModifiedBy": "string",
    "lastModifiedByType": "User"
  }
}

Status code:

201

{
  "id": "string",
  "name": "string",
  "type": "string",
  "properties": {
    "description": "string",
    "tags": {
      "string": "string"
    },
    "properties": {
      "string": "string"
    },
    "displayName": "string",
    "status": "NotStarted",
    "experimentName": "string",
    "services": {
      "string": {
        "jobServiceType": "string",
        "port": 1,
        "endpoint": "string",
        "status": "string",
        "errorMessage": "string",
        "properties": {
          "string": "string"
        }
      }
    },
    "computeId": "string",
    "jobType": "Pipeline",
    "settings": {},
    "inputs": {
      "string": {
        "description": "string",
        "jobInputType": "literal",
        "value": "string"
      }
    },
    "outputs": {
      "string": {
        "description": "string",
        "jobOutputType": "uri_file",
        "mode": "Upload",
        "uri": "string"
      }
    }
  },
  "systemData": {
    "createdAt": "2020-01-01T12:34:56.999Z",
    "createdBy": "string",
    "createdByType": "User",
    "lastModifiedAt": "2020-01-01T12:34:56.999Z",
    "lastModifiedBy": "string",
    "lastModifiedByType": "User"
  }
}

CreateOrUpdate Sweep Job.

Sample request

HTTP

PUT https://management.azure.com/subscriptions/00000000-1111-2222-3333-444444444444/resourceGroups/test-rg/providers/Microsoft.MachineLearningServices/workspaces/my-aml-workspace/jobs/string?api-version=2023-10-01

{
  "properties": {
    "description": "string",
    "tags": {
      "string": "string"
    },
    "properties": {
      "string": "string"
    },
    "displayName": "string",
    "experimentName": "string",
    "services": {
      "string": {
        "jobServiceType": "string",
        "port": 1,
        "endpoint": "string",
        "properties": {
          "string": "string"
        }
      }
    },
    "computeId": "string",
    "jobType": "Sweep",
    "samplingAlgorithm": {
      "samplingAlgorithmType": "Grid"
    },
    "limits": {
      "jobLimitsType": "Sweep",
      "maxTotalTrials": 1,
      "maxConcurrentTrials": 1,
      "trialTimeout": "PT1S"
    },
    "earlyTermination": {
      "evaluationInterval": 1,
      "delayEvaluation": 1,
      "policyType": "MedianStopping"
    },
    "objective": {
      "primaryMetric": "string",
      "goal": "Minimize"
    },
    "trial": {
      "codeId": "string",
      "command": "string",
      "environmentId": "string",
      "environmentVariables": {
        "string": "string"
      },
      "distribution": {
        "distributionType": "Mpi",
        "processCountPerInstance": 1
      },
      "resources": {
        "instanceCount": 1,
        "instanceType": "string",
        "properties": {
          "string": {
            "e6b6493e-7d5e-4db3-be1e-306ec641327e": null
          }
        }
      }
    },
    "searchSpace": {
      "string": {}
    }
  }
}

Sample response

Status code:

200

{
  "id": "string",
  "name": "string",
  "type": "string",
  "properties": {
    "description": "string",
    "tags": {
      "string": "string"
    },
    "properties": {
      "string": "string"
    },
    "displayName": "string",
    "status": "NotStarted",
    "experimentName": "string",
    "services": {
      "string": {
        "jobServiceType": "string",
        "port": 1,
        "endpoint": "string",
        "status": "string",
        "errorMessage": "string",
        "properties": {
          "string": "string"
        }
      }
    },
    "computeId": "string",
    "jobType": "Sweep",
    "samplingAlgorithm": {
      "samplingAlgorithmType": "Grid"
    },
    "limits": {
      "jobLimitsType": "Sweep",
      "maxTotalTrials": 1,
      "maxConcurrentTrials": 1,
      "trialTimeout": "PT1S"
    },
    "earlyTermination": {
      "evaluationInterval": 1,
      "delayEvaluation": 1,
      "policyType": "MedianStopping"
    },
    "objective": {
      "primaryMetric": "string",
      "goal": "Minimize"
    },
    "trial": {
      "codeId": "string",
      "command": "string",
      "environmentId": "string",
      "environmentVariables": {
        "string": "string"
      },
      "distribution": {
        "distributionType": "Mpi",
        "processCountPerInstance": 1
      },
      "resources": {
        "instanceCount": 1,
        "instanceType": "string",
        "properties": {
          "string": {
            "e6b6493e-7d5e-4db3-be1e-306ec641327e": null
          }
        }
      }
    },
    "searchSpace": {
      "string": {}
    }
  },
  "systemData": {
    "createdAt": "2020-01-01T12:34:56.999Z",
    "createdBy": "string",
    "createdByType": "User",
    "lastModifiedAt": "2020-01-01T12:34:56.999Z",
    "lastModifiedBy": "string",
    "lastModifiedByType": "User"
  }
}

Status code:

201

{
  "id": "string",
  "name": "string",
  "type": "string",
  "properties": {
    "description": "string",
    "tags": {
      "string": "string"
    },
    "properties": {
      "string": "string"
    },
    "displayName": "string",
    "status": "NotStarted",
    "experimentName": "string",
    "services": {
      "string": {
        "jobServiceType": "string",
        "port": 1,
        "endpoint": "string",
        "status": "string",
        "errorMessage": "string",
        "properties": {
          "string": "string"
        }
      }
    },
    "computeId": "string",
    "jobType": "Sweep",
    "samplingAlgorithm": {
      "samplingAlgorithmType": "Grid"
    },
    "limits": {
      "jobLimitsType": "Sweep",
      "maxTotalTrials": 1,
      "maxConcurrentTrials": 1,
      "trialTimeout": "PT1S"
    },
    "earlyTermination": {
      "evaluationInterval": 1,
      "delayEvaluation": 1,
      "policyType": "MedianStopping"
    },
    "objective": {
      "primaryMetric": "string",
      "goal": "Minimize"
    },
    "trial": {
      "codeId": "string",
      "command": "string",
      "environmentId": "string",
      "environmentVariables": {
        "string": "string"
      },
      "distribution": {
        "distributionType": "Mpi",
        "processCountPerInstance": 1
      },
      "resources": {
        "instanceCount": 1,
        "instanceType": "string",
        "properties": {
          "string": {
            "e6b6493e-7d5e-4db3-be1e-306ec641327e": null
          }
        }
      }
    },
    "searchSpace": {
      "string": {}
    }
  },
  "systemData": {
    "createdAt": "2020-01-01T12:34:56.999Z",
    "createdBy": "string",
    "createdByType": "User",
    "lastModifiedAt": "2020-01-01T12:34:56.999Z",
    "lastModifiedBy": "string",
    "lastModifiedByType": "User"
  }
}

Definitions

Name	Description
AllNodes	All nodes means the service will be running on all of the nodes of the job
AmlToken	AML Token identity configuration.
AutoForecastHorizon	Forecast horizon determined automatically by system.
AutoMLJob	AutoMLJob class. Use this class for executing AutoML tasks like Classification/Regression etc. See TaskType enum for all the tasks supported.
AutoNCrossValidations	N-Cross validations determined automatically.
AutoSeasonality
AutoTargetLags
AutoTargetRollingWindowSize	Target lags rolling window determined automatically.
BanditPolicy	Defines an early termination policy based on slack criteria, and a frequency and delay interval for evaluation
BayesianSamplingAlgorithm	Defines a Sampling Algorithm that generates values based on previous values
BlockedTransformers	Enum for all classification models supported by AutoML.
Classification	Classification task in AutoML Table vertical.
ClassificationModels	Enum for all classification models supported by AutoML.
ClassificationMultilabelPrimaryMetrics	Primary metrics for classification multilabel tasks.
ClassificationPrimaryMetrics	Primary metrics for classification tasks.
ClassificationTrainingSettings	Classification Training related configuration.
CommandJob	Command job definition.
CommandJobLimits	Command Job limit class.
createdByType	The type of identity that created the resource.
CustomForecastHorizon	The desired maximum forecast horizon in units of time-series frequency.
CustomModelJobInput
CustomModelJobOutput
CustomNCrossValidations	N-Cross validations are specified by user.
CustomSeasonality
CustomTargetLags
CustomTargetRollingWindowSize
DistributionType	Enum to determine the job distribution type.
EarlyTerminationPolicyType
ErrorAdditionalInfo	The resource management error additional info.
ErrorDetail	The error detail.
ErrorResponse	Error response
FeatureLags	Flag for generating lags for the numeric features.
FeaturizationMode	Featurization mode - determines data featurization mode.
ForecastHorizonMode	Enum to determine forecast horizon selection mode.
Forecasting	Forecasting task in AutoML Table vertical.
ForecastingModels	Enum for all forecasting models supported by AutoML.
ForecastingPrimaryMetrics	Primary metrics for Forecasting task.
ForecastingSettings	Forecasting specific parameters.
ForecastingTrainingSettings	Forecasting Training related configuration.
Goal	Defines supported metric goals for hyperparameter tuning
GridSamplingAlgorithm	Defines a Sampling Algorithm that exhaustively generates every value combination in the space
IdentityConfigurationType	Enum to determine identity framework.
ImageClassification	Image Classification. Multi-class image classification is used when an image is classified with only a single label from a set of classes - e.g. each image is classified as either an image of a 'cat' or a 'dog' or a 'duck'.
ImageClassificationMultilabel	Image Classification Multilabel. Multi-label image classification is used when an image could have one or more labels from a set of labels - e.g. an image could be labeled with both 'cat' and 'dog'.
ImageInstanceSegmentation	Image Instance Segmentation. Instance segmentation is used to identify objects in an image at the pixel level, drawing a polygon around each object in the image.
ImageLimitSettings	Limit settings for the AutoML job.
ImageModelDistributionSettingsClassification	Distribution expressions to sweep over values of model settings. Some examples are: ModelName = "choice('seresnext', 'resnest50')"; LearningRate = "uniform(0.001, 0.01)"; LayersToFreeze = "choice(0, 2)"; ```</example> For more details on how to compose distribution expressions please check the documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters For more information on the available settings please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
ImageModelDistributionSettingsObjectDetection	Distribution expressions to sweep over values of model settings. Some examples are: ModelName = "choice('seresnext', 'resnest50')"; LearningRate = "uniform(0.001, 0.01)"; LayersToFreeze = "choice(0, 2)"; ```</example> For more details on how to compose distribution expressions please check the documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters For more information on the available settings please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
ImageModelSettingsClassification	Settings used for training the model. For more information on the available settings please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
ImageModelSettingsObjectDetection	Settings used for training the model. For more information on the available settings please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
ImageObjectDetection	Image Object Detection. Object detection is used to identify objects in an image and locate each object with a bounding box e.g. locate all dogs and cats in an image and draw a bounding box around each.
ImageSweepSettings	Model sweeping and hyperparameter sweeping related settings.
InputDeliveryMode	Enum to determine the input data delivery mode.
InstanceSegmentationPrimaryMetrics	Primary metrics for InstanceSegmentation tasks.
JobBaseResource	Azure Resource Manager resource envelope.
JobInputType	Enum to determine the Job Input Type.
JobLimitsType
JobOutputType	Enum to determine the Job Output Type.
JobResourceConfiguration
JobService	Job endpoint definition
JobStatus	The status of a job.
JobTier	Enum to determine the job tier.
JobType	Enum to determine the type of job.
LearningRateScheduler	Learning rate scheduler enum.
LiteralJobInput	Literal input type.
LogVerbosity	Enum for setting log verbosity.
ManagedIdentity	Managed identity configuration.
MedianStoppingPolicy	Defines an early termination policy based on running averages of the primary metric of all runs
MLFlowModelJobInput
MLFlowModelJobOutput
MLTableJobInput
MLTableJobOutput
ModelSize	Image model size.
Mpi	MPI distribution configuration.
NCrossValidationsMode	Determines how N-Cross validations value is determined.
NlpVerticalFeaturizationSettings
NlpVerticalLimitSettings	Job execution constraints.
NodesValueType	The enumerated types for the nodes value
ObjectDetectionPrimaryMetrics	Primary metrics for Image ObjectDetection task.
Objective	Optimization objective.
OutputDeliveryMode	Output data delivery mode enums.
PipelineJob	Pipeline Job definition: defines generic to MFE attributes.
PyTorch	PyTorch distribution configuration.
QueueSettings
RandomSamplingAlgorithm	Defines a Sampling Algorithm that generates values randomly
RandomSamplingAlgorithmRule	The specific type of random algorithm
Regression	Regression task in AutoML Table vertical.
RegressionModels	Enum for all Regression models supported by AutoML.
RegressionPrimaryMetrics	Primary metrics for Regression task.
RegressionTrainingSettings	Regression Training related configuration.
SamplingAlgorithmType
SeasonalityMode	Forecasting seasonality mode.
ShortSeriesHandlingConfiguration	The parameter defining how if AutoML should handle short time series.
StackEnsembleSettings	Advances setting to customize StackEnsemble run.
StackMetaLearnerType	The meta-learner is a model trained on the output of the individual heterogeneous models. Default meta-learners are LogisticRegression for classification tasks (or LogisticRegressionCV if cross-validation is enabled) and ElasticNet for regression/forecasting tasks (or ElasticNetCV if cross-validation is enabled). This parameter can be one of the following strings: LogisticRegression, LogisticRegressionCV, LightGBMClassifier, ElasticNet, ElasticNetCV, LightGBMRegressor, or LinearRegression
StochasticOptimizer	Stochastic optimizer for image models.
SweepJob	Sweep job definition.
SweepJobLimits	Sweep Job limit class.
systemData	Metadata pertaining to creation and last modification of the resource.
TableVerticalFeaturizationSettings	Featurization Configuration.
TableVerticalLimitSettings	Job execution constraints.
TargetAggregationFunction	Target aggregate function.
TargetLagsMode	Target lags selection modes.
TargetRollingWindowSizeMode	Target rolling windows size mode.
TaskType	AutoMLJob Task type.
TensorFlow	TensorFlow distribution configuration.
TextClassification	Text Classification task in AutoML NLP vertical. NLP - Natural Language Processing.
TextClassificationMultilabel	Text Classification Multilabel task in AutoML NLP vertical. NLP - Natural Language Processing.
TextNer	Text-NER task in AutoML NLP vertical. NER - Named Entity Recognition. NLP - Natural Language Processing.
TrialComponent	Trial component definition.
TritonModelJobInput
TritonModelJobOutput
TruncationSelectionPolicy	Defines an early termination policy that cancels a given percentage of runs at each evaluation interval.
UriFileJobInput
UriFileJobOutput
UriFolderJobInput
UriFolderJobOutput
UserIdentity	User identity configuration.
UseStl	Configure STL Decomposition of the time-series target column.
ValidationMetricType	Metric computation method to use for validation metrics in image tasks.

AllNodes

All nodes means the service will be running on all of the nodes of the job

Name	Type	Description
nodesValueType	string: All	[Required] Type of the Nodes value

AmlToken

AML Token identity configuration.

Name	Type	Description
identityType	string: AMLToken	[Required] Specifies the type of identity framework.

AutoForecastHorizon

Forecast horizon determined automatically by system.

Name	Type	Description
mode	string: Auto	[Required] Set forecast horizon value selection mode.

AutoMLJob

AutoMLJob class. Use this class for executing AutoML tasks like Classification/Regression etc. See TaskType enum for all the tasks supported.

Name	Type	Default value	Description
componentId	string		ARM resource ID of the component resource.
computeId	string		ARM resource ID of the compute resource.
description	string		The asset description text.
displayName	string		Display name of job.
environmentId	string		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.
environmentVariables	object		Environment variables included in the job.
experimentName	string	Default	The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
identity	IdentityConfiguration: AmlToken ManagedIdentity UserIdentity		Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
isArchived	boolean	False	Is the asset archived?
jobType	string: AutoML		[Required] Specifies the type of job.
outputs	object		Mapping of output data bindings used in the job.
properties	object		The asset property dictionary.
queueSettings	QueueSettings		Queue settings for the job
resources	JobResourceConfiguration	{}	Compute Resource configuration for the job.
services	<string,  JobService>		List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
status	JobStatus		Status of the job.
tags	object		Tag dictionary. Tags can be added, removed, and updated.
taskDetails	AutoMLVertical: Classification Forecasting ImageClassification ImageClassificationMultilabel ImageInstanceSegmentation ImageObjectDetection Regression TextClassification TextClassificationMultilabel TextNer		[Required] This represents scenario which can be one of Tables/NLP/Image

AutoNCrossValidations

N-Cross validations determined automatically.

Name	Type	Description
mode	string: Auto	[Required] Mode for determining N-Cross validations.

AutoSeasonality

Name	Type	Description
mode	string: Auto	[Required] Seasonality mode.

AutoTargetLags

Name	Type	Description
mode	string: Auto	[Required] Set target lags mode - Auto/Custom

AutoTargetRollingWindowSize

Target lags rolling window determined automatically.

Name	Type	Description
mode	string: Auto	[Required] TargetRollingWindowSiz detection mode.

BanditPolicy

Defines an early termination policy based on slack criteria, and a frequency and delay interval for evaluation

Name	Type	Default value	Description
delayEvaluation	integer	0	Number of intervals by which to delay the first evaluation.
evaluationInterval	integer	0	Interval (number of runs) between policy evaluations.
policyType	string: Bandit		[Required] Name of policy configuration
slackAmount	number	0	Absolute distance allowed from the best performing run.
slackFactor	number	0	Ratio of the allowed distance from the best performing run.

BayesianSamplingAlgorithm

Defines a Sampling Algorithm that generates values based on previous values

Name	Type	Description
samplingAlgorithmType	string: Bayesian	[Required] The algorithm used for generating hyperparameter values, along with configuration properties

BlockedTransformers

Enum for all classification models supported by AutoML.

Name	Type	Description
CatTargetEncoder	string	Target encoding for categorical data.
CountVectorizer	string	Count Vectorizer converts a collection of text documents to a matrix of token counts.
HashOneHotEncoder	string	Hashing One Hot Encoder can turn categorical variables into a limited number of new features. This is often used for high-cardinality categorical features.
LabelEncoder	string	Label encoder converts labels/categorical variables in a numerical form.
NaiveBayes	string	Naive Bayes is a classified that is used for classification of discrete features that are categorically distributed.
OneHotEncoder	string	Ohe hot encoding creates a binary feature transformation.
TextTargetEncoder	string	Target encoding for text data.
TfIdf	string	Tf-Idf stands for, term-frequency times inverse document-frequency. This is a common term weighting scheme for identifying information from documents.
WoETargetEncoder	string	Weight of Evidence encoding is a technique used to encode categorical variables. It uses the natural log of the P(1)/P(0) to create weights.
WordEmbedding	string	Word embedding helps represents words or phrases as a vector, or a series of numbers.

Classification

Classification task in AutoML Table vertical.

Name	Type	Default value	Description
cvSplitColumnNames	string[]		Columns to use for CVSplit data.
featurizationSettings	TableVerticalFeaturizationSettings		Featurization inputs needed for AutoML job.
limitSettings	TableVerticalLimitSettings		Execution constraints for AutoMLJob.
logVerbosity	LogVerbosity	Info	Log verbosity for the job.
nCrossValidations	NCrossValidations: AutoNCrossValidations CustomNCrossValidations		Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
positiveLabel	string		Positive label for binary metrics calculation.
primaryMetric	ClassificationPrimaryMetrics	AUCWeighted	Primary metric for the task.
targetColumnName	string		Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
taskType	string: Classification		[Required] Task type for AutoMLJob.
testData	MLTableJobInput		Test data input.
testDataSize	number		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.
trainingData	MLTableJobInput		[Required] Training data input.
trainingSettings	ClassificationTrainingSettings		Inputs for training phase for an AutoML Job.
validationData	MLTableJobInput		Validation data inputs.
validationDataSize	number		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.
weightColumnName	string		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.

ClassificationModels

Enum for all classification models supported by AutoML.

Name	Type	Description
BernoulliNaiveBayes	string	Naive Bayes classifier for multivariate Bernoulli models.
DecisionTree	string	Decision Trees are a non-parametric supervised learning method used for both classification and regression tasks. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.
ExtremeRandomTrees	string	Extreme Trees is an ensemble machine learning algorithm that combines the predictions from many decision trees. It is related to the widely used random forest algorithm.
GradientBoosting	string	The technique of transiting week learners into a strong learner is called Boosting. The gradient boosting algorithm process works on this theory of execution.
KNN	string	K-nearest neighbors (KNN) algorithm uses 'feature similarity' to predict the values of new datapoints which further means that the new data point will be assigned a value based on how closely it matches the points in the training set.
LightGBM	string	LightGBM is a gradient boosting framework that uses tree based learning algorithms.
LinearSVM	string	A support vector machine (SVM) is a supervised machine learning model that uses classification algorithms for two-group classification problems. After giving an SVM model sets of labeled training data for each category, they're able to categorize new text. Linear SVM performs best when input data is linear, i.e., data can be easily classified by drawing the straight line between classified values on a plotted graph.
LogisticRegression	string	Logistic regression is a fundamental classification technique. It belongs to the group of linear classifiers and is somewhat similar to polynomial and linear regression. Logistic regression is fast and relatively uncomplicated, and it's convenient for you to interpret the results. Although it's essentially a method for binary classification, it can also be applied to multiclass problems.
MultinomialNaiveBayes	string	The multinomial Naive Bayes classifier is suitable for classification with discrete features (e.g., word counts for text classification). The multinomial distribution normally requires integer feature counts. However, in practice, fractional counts such as tf-idf may also work.
RandomForest	string	Random forest is a supervised learning algorithm. The "forest" it builds, is an ensemble of decision trees, usually trained with the “bagging” method. The general idea of the bagging method is that a combination of learning models increases the overall result.
SGD	string	SGD: Stochastic gradient descent is an optimization algorithm often used in machine learning applications to find the model parameters that correspond to the best fit between predicted and actual outputs.
SVM	string	A support vector machine (SVM) is a supervised machine learning model that uses classification algorithms for two-group classification problems. After giving an SVM model sets of labeled training data for each category, they're able to categorize new text.
XGBoostClassifier	string	XGBoost: Extreme Gradient Boosting Algorithm. This algorithm is used for structured data where target column values can be divided into distinct class values.

ClassificationMultilabelPrimaryMetrics

Primary metrics for classification multilabel tasks.

Name	Type	Description
AUCWeighted	string	AUC is the Area under the curve. This metric represents arithmetic mean of the score for each class, weighted by the number of true instances in each class.
Accuracy	string	Accuracy is the ratio of predictions that exactly match the true class labels.
AveragePrecisionScoreWeighted	string	The arithmetic mean of the average precision score for each class, weighted by the number of true instances in each class.
IOU	string	Intersection Over Union. Intersection of predictions divided by union of predictions.
NormMacroRecall	string	Normalized macro recall is recall macro-averaged and normalized, so that random performance has a score of 0, and perfect performance has a score of 1.
PrecisionScoreWeighted	string	The arithmetic mean of precision for each class, weighted by number of true instances in each class.

ClassificationPrimaryMetrics

Primary metrics for classification tasks.

Name	Type	Description
AUCWeighted	string	AUC is the Area under the curve. This metric represents arithmetic mean of the score for each class, weighted by the number of true instances in each class.
Accuracy	string	Accuracy is the ratio of predictions that exactly match the true class labels.
AveragePrecisionScoreWeighted	string	The arithmetic mean of the average precision score for each class, weighted by the number of true instances in each class.
NormMacroRecall	string	Normalized macro recall is recall macro-averaged and normalized, so that random performance has a score of 0, and perfect performance has a score of 1.
PrecisionScoreWeighted	string	The arithmetic mean of precision for each class, weighted by number of true instances in each class.

ClassificationTrainingSettings

Classification Training related configuration.

Name	Type	Default value	Description
allowedTrainingAlgorithms	ClassificationModels[]		Allowed models for classification task.
blockedTrainingAlgorithms	ClassificationModels[]		Blocked models for classification task.
enableDnnTraining	boolean	False	Enable recommendation of DNN models.
enableModelExplainability	boolean	True	Flag to turn on explainability on best model.
enableOnnxCompatibleModels	boolean	False	Flag for enabling onnx compatible models.
enableStackEnsemble	boolean	True	Enable stack ensemble run.
enableVoteEnsemble	boolean	True	Enable voting ensemble run.
ensembleModelDownloadTimeout	string	PT5M	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.
stackEnsembleSettings	StackEnsembleSettings		Stack ensemble settings for stack ensemble run.

CommandJob

Command job definition.

Name	Type	Default value	Description
codeId	string		ARM resource ID of the code asset.
command	string		[Required] The command to execute on startup of the job. eg. "python train.py"
componentId	string		ARM resource ID of the component resource.
computeId	string		ARM resource ID of the compute resource.
description	string		The asset description text.
displayName	string		Display name of job.
distribution	DistributionConfiguration: Mpi PyTorch TensorFlow		Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
environmentId	string		[Required] The ARM resource ID of the Environment specification for the job.
environmentVariables	object		Environment variables included in the job.
experimentName	string	Default	The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
identity	IdentityConfiguration: AmlToken ManagedIdentity UserIdentity		Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
inputs	object		Mapping of input data bindings used in the job.
isArchived	boolean	False	Is the asset archived?
jobType	string: Command		[Required] Specifies the type of job.
limits	CommandJobLimits		Command Job limit.
outputs	object		Mapping of output data bindings used in the job.
parameters	object		Input parameters.
properties	object		The asset property dictionary.
queueSettings	QueueSettings		Queue settings for the job
resources	JobResourceConfiguration	{}	Compute Resource configuration for the job.
services	<string,  JobService>		List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
status	JobStatus		Status of the job.
tags	object		Tag dictionary. Tags can be added, removed, and updated.

CommandJobLimits

Command Job limit class.

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

createdByType

The type of identity that created the resource.

Name	Type	Description
Application	string
Key	string
ManagedIdentity	string
User	string

CustomForecastHorizon

The desired maximum forecast horizon in units of time-series frequency.

Name	Type	Description
mode	string: Custom	[Required] Set forecast horizon value selection mode.
value	integer	[Required] Forecast horizon value.

CustomModelJobInput

Name	Type	Default value	Description
description	string		Description for the input.
jobInputType	string: custom_model		[Required] Specifies the type of job.
mode	InputDeliveryMode	ReadOnlyMount	Input Asset Delivery Mode.
uri	string		[Required] Input Asset URI.

CustomModelJobOutput

Name	Type	Default value	Description
description	string		Description for the output.
jobOutputType	string: custom_model		[Required] Specifies the type of job.
mode	OutputDeliveryMode	ReadWriteMount	Output Asset Delivery Mode.
uri	string		Output Asset URI.

CustomNCrossValidations

N-Cross validations are specified by user.

Name	Type	Description
mode	string: Custom	[Required] Mode for determining N-Cross validations.
value	integer	[Required] N-Cross validations value.

CustomSeasonality

Name	Type	Description
mode	string: Custom	[Required] Seasonality mode.
value	integer	[Required] Seasonality value.

CustomTargetLags

Name	Type	Description
mode	string: Custom	[Required] Set target lags mode - Auto/Custom
values	integer[]	[Required] Set target lags values.

CustomTargetRollingWindowSize

Name	Type	Description
mode	string: Custom	[Required] TargetRollingWindowSiz detection mode.
value	integer	[Required] TargetRollingWindowSize value.

DistributionType

Enum to determine the job distribution type.

Name	Type	Description
Mpi	string
PyTorch	string
TensorFlow	string

EarlyTerminationPolicyType

Name	Type	Description
Bandit	string
MedianStopping	string
TruncationSelection	string

ErrorAdditionalInfo

The resource management error additional info.

Name	Type	Description
info	object	The additional info.
type	string	The additional info type.

ErrorDetail

The error detail.

Name	Type	Description
additionalInfo	ErrorAdditionalInfo[]	The error additional info.
code	string	The error code.
details	ErrorDetail[]	The error details.
message	string	The error message.
target	string	The error target.

ErrorResponse

Error response

Name	Type	Description
error	ErrorDetail	The error object.

FeatureLags

Flag for generating lags for the numeric features.

Name	Type	Description
Auto	string	System auto-generates feature lags.
None	string	No feature lags generated.

FeaturizationMode

Featurization mode - determines data featurization mode.

Name	Type	Description
Auto	string	Auto mode, system performs featurization without any custom featurization inputs.
Custom	string	Custom featurization.
Off	string	Featurization off. 'Forecasting' task cannot use this value.

ForecastHorizonMode

Enum to determine forecast horizon selection mode.

Name	Type	Description
Auto	string	Forecast horizon to be determined automatically.
Custom	string	Use the custom forecast horizon.

Forecasting

Forecasting task in AutoML Table vertical.

Name	Type	Default value	Description
cvSplitColumnNames	string[]		Columns to use for CVSplit data.
featurizationSettings	TableVerticalFeaturizationSettings		Featurization inputs needed for AutoML job.
forecastingSettings	ForecastingSettings		Forecasting task specific inputs.
limitSettings	TableVerticalLimitSettings		Execution constraints for AutoMLJob.
logVerbosity	LogVerbosity	Info	Log verbosity for the job.
nCrossValidations	NCrossValidations: AutoNCrossValidations CustomNCrossValidations		Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
primaryMetric	ForecastingPrimaryMetrics	NormalizedRootMeanSquaredError	Primary metric for forecasting task.
targetColumnName	string		Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
taskType	string: Forecasting		[Required] Task type for AutoMLJob.
testData	MLTableJobInput		Test data input.
testDataSize	number		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.
trainingData	MLTableJobInput		[Required] Training data input.
trainingSettings	ForecastingTrainingSettings		Inputs for training phase for an AutoML Job.
validationData	MLTableJobInput		Validation data inputs.
validationDataSize	number		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.
weightColumnName	string		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.

ForecastingModels

Enum for all forecasting models supported by AutoML.

Name	Type	Description
Arimax	string	An Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) model can be viewed as a multiple regression model with one or more autoregressive (AR) terms and/or one or more moving average (MA) terms. This method is suitable for forecasting when data is stationary/non stationary, and multivariate with any type of data pattern, i.e., level/trend /seasonality/cyclicity.
AutoArima	string	Auto-Autoregressive Integrated Moving Average (ARIMA) model uses time-series data and statistical analysis to interpret the data and make future predictions. This model aims to explain data by using time series data on its past values and uses linear regression to make predictions.
Average	string	The Average forecasting model makes predictions by carrying forward the average of the target values for each time-series in the training data.
DecisionTree	string	Decision Trees are a non-parametric supervised learning method used for both classification and regression tasks. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.
ElasticNet	string	Elastic net is a popular type of regularized linear regression that combines two popular penalties, specifically the L1 and L2 penalty functions.
ExponentialSmoothing	string	Exponential smoothing is a time series forecasting method for univariate data that can be extended to support data with a systematic trend or seasonal component.
ExtremeRandomTrees	string	Extreme Trees is an ensemble machine learning algorithm that combines the predictions from many decision trees. It is related to the widely used random forest algorithm.
GradientBoosting	string	The technique of transiting week learners into a strong learner is called Boosting. The gradient boosting algorithm process works on this theory of execution.
KNN	string	K-nearest neighbors (KNN) algorithm uses 'feature similarity' to predict the values of new datapoints which further means that the new data point will be assigned a value based on how closely it matches the points in the training set.
LassoLars	string	Lasso model fit with Least Angle Regression a.k.a. Lars. It is a Linear Model trained with an L1 prior as regularizer.
LightGBM	string	LightGBM is a gradient boosting framework that uses tree based learning algorithms.
Naive	string	The Naive forecasting model makes predictions by carrying forward the latest target value for each time-series in the training data.
Prophet	string	Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.
RandomForest	string	Random forest is a supervised learning algorithm. The "forest" it builds, is an ensemble of decision trees, usually trained with the “bagging” method. The general idea of the bagging method is that a combination of learning models increases the overall result.
SGD	string	SGD: Stochastic gradient descent is an optimization algorithm often used in machine learning applications to find the model parameters that correspond to the best fit between predicted and actual outputs. It's an inexact but powerful technique.
SeasonalAverage	string	The Seasonal Average forecasting model makes predictions by carrying forward the average value of the latest season of data for each time-series in the training data.
SeasonalNaive	string	The Seasonal Naive forecasting model makes predictions by carrying forward the latest season of target values for each time-series in the training data.
TCNForecaster	string	TCNForecaster: Temporal Convolutional Networks Forecaster. //TODO: Ask forecasting team for brief intro.
XGBoostRegressor	string	XGBoostRegressor: Extreme Gradient Boosting Regressor is a supervised machine learning model using ensemble of base learners.

ForecastingPrimaryMetrics

Primary metrics for Forecasting task.

Name	Type	Description
NormalizedMeanAbsoluteError	string	The Normalized Mean Absolute Error (NMAE) is a validation metric to compare the Mean Absolute Error (MAE) of (time) series with different scales.
NormalizedRootMeanSquaredError	string	The Normalized Root Mean Squared Error (NRMSE) the RMSE facilitates the comparison between models with different scales.
R2Score	string	The R2 score is one of the performance evaluation measures for forecasting-based machine learning models.
SpearmanCorrelation	string	The Spearman's rank coefficient of correlation is a non-parametric measure of rank correlation.

ForecastingSettings

Forecasting specific parameters.

Name	Type	Default value	Description
countryOrRegionForHolidays	string		Country or region for holidays for forecasting tasks. These should be ISO 3166 two-letter country/region codes, for example 'US' or 'GB'.
cvStepSize	integer		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.
featureLags	FeatureLags	None	Flag for generating lags for the numeric features with 'auto' or null.
forecastHorizon	ForecastHorizon: AutoForecastHorizon CustomForecastHorizon	{"Mode": "Custom", "Value": 1}	The desired maximum forecast horizon in units of time-series frequency.
frequency	string		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.
seasonality	Seasonality: AutoSeasonality CustomSeasonality	{"Mode": "Auto"}	Set time series seasonality as an integer multiple of the series frequency. If seasonality is set to 'auto', it will be inferred.
shortSeriesHandlingConfig	ShortSeriesHandlingConfiguration	Auto	The parameter defining how if AutoML should handle short time series.
targetAggregateFunction	TargetAggregationFunction	None	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".
targetLags	TargetLags: AutoTargetLags CustomTargetLags		The number of past periods to lag from the target column.
targetRollingWindowSize	TargetRollingWindowSize: AutoTargetRollingWindowSize CustomTargetRollingWindowSize		The number of past periods used to create a rolling window average of the target column.
timeColumnName	string		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.
timeSeriesIdColumnNames	string[]		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.
useStl	UseStl	None	Configure STL Decomposition of the time-series target column.

ForecastingTrainingSettings

Forecasting Training related configuration.

Name	Type	Default value	Description
allowedTrainingAlgorithms	ForecastingModels[]		Allowed models for forecasting task.
blockedTrainingAlgorithms	ForecastingModels[]		Blocked models for forecasting task.
enableDnnTraining	boolean	False	Enable recommendation of DNN models.
enableModelExplainability	boolean	True	Flag to turn on explainability on best model.
enableOnnxCompatibleModels	boolean	False	Flag for enabling onnx compatible models.
enableStackEnsemble	boolean	True	Enable stack ensemble run.
enableVoteEnsemble	boolean	True	Enable voting ensemble run.
ensembleModelDownloadTimeout	string	PT5M	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.
stackEnsembleSettings	StackEnsembleSettings		Stack ensemble settings for stack ensemble run.

Goal

Defines supported metric goals for hyperparameter tuning

Name	Type	Description
Maximize	string
Minimize	string

GridSamplingAlgorithm

Defines a Sampling Algorithm that exhaustively generates every value combination in the space

Name	Type	Description
samplingAlgorithmType	string: Grid	[Required] The algorithm used for generating hyperparameter values, along with configuration properties

IdentityConfigurationType

Enum to determine identity framework.

Name	Type	Description
AMLToken	string
Managed	string
UserIdentity	string

ImageClassification

Image Classification. Multi-class image classification is used when an image is classified with only a single label from a set of classes - e.g. each image is classified as either an image of a 'cat' or a 'dog' or a 'duck'.

Name	Type	Default value	Description
limitSettings	ImageLimitSettings		[Required] Limit settings for the AutoML job.
logVerbosity	LogVerbosity	Info	Log verbosity for the job.
modelSettings	ImageModelSettingsClassification		Settings used for training the model.
primaryMetric	ClassificationPrimaryMetrics	Accuracy	Primary metric to optimize for this task.
searchSpace	ImageModelDistributionSettingsClassification[]		Search space for sampling different combinations of models and their hyperparameters.
sweepSettings	ImageSweepSettings		Model sweeping and hyperparameter sweeping related settings.
targetColumnName	string		Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
taskType	string: ImageClassification		[Required] Task type for AutoMLJob.
trainingData	MLTableJobInput		[Required] Training data input.
validationData	MLTableJobInput		Validation data inputs.
validationDataSize	number		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.

ImageClassificationMultilabel

Image Classification Multilabel. Multi-label image classification is used when an image could have one or more labels from a set of labels - e.g. an image could be labeled with both 'cat' and 'dog'.

Name	Type	Default value	Description
limitSettings	ImageLimitSettings		[Required] Limit settings for the AutoML job.
logVerbosity	LogVerbosity	Info	Log verbosity for the job.
modelSettings	ImageModelSettingsClassification		Settings used for training the model.
primaryMetric	ClassificationMultilabelPrimaryMetrics	IOU	Primary metric to optimize for this task.
searchSpace	ImageModelDistributionSettingsClassification[]		Search space for sampling different combinations of models and their hyperparameters.
sweepSettings	ImageSweepSettings		Model sweeping and hyperparameter sweeping related settings.
targetColumnName	string		Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
taskType	string: ImageClassificationMultilabel		[Required] Task type for AutoMLJob.
trainingData	MLTableJobInput		[Required] Training data input.
validationData	MLTableJobInput		Validation data inputs.
validationDataSize	number		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.

ImageInstanceSegmentation

Image Instance Segmentation. Instance segmentation is used to identify objects in an image at the pixel level, drawing a polygon around each object in the image.

Name	Type	Default value	Description
limitSettings	ImageLimitSettings		[Required] Limit settings for the AutoML job.
logVerbosity	LogVerbosity	Info	Log verbosity for the job.
modelSettings	ImageModelSettingsObjectDetection		Settings used for training the model.
primaryMetric	InstanceSegmentationPrimaryMetrics	MeanAveragePrecision	Primary metric to optimize for this task.
searchSpace	ImageModelDistributionSettingsObjectDetection[]		Search space for sampling different combinations of models and their hyperparameters.
sweepSettings	ImageSweepSettings		Model sweeping and hyperparameter sweeping related settings.
targetColumnName	string		Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
taskType	string: ImageInstanceSegmentation		[Required] Task type for AutoMLJob.
trainingData	MLTableJobInput		[Required] Training data input.
validationData	MLTableJobInput		Validation data inputs.
validationDataSize	number		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.

ImageLimitSettings

Limit settings for the AutoML job.

Name	Type	Default value	Description
maxConcurrentTrials	integer	1	Maximum number of concurrent AutoML iterations.
maxTrials	integer	1	Maximum number of AutoML iterations.
timeout	string	P7D	AutoML job timeout.

ImageModelDistributionSettingsClassification

Distribution expressions to sweep over values of model settings. Some examples are:

ModelName = "choice('seresnext', 'resnest50')";
LearningRate = "uniform(0.001, 0.01)";
LayersToFreeze = "choice(0, 2)";
```</example>
For more details on how to compose distribution expressions please check the documentation:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters
For more information on the available settings please visit the official documentation:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.

Name	Type	Description
amsGradient	string	Enable AMSGrad when optimizer is 'adam' or 'adamw'.
augmentations	string	Settings for using Augmentations.
beta1	string	Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
beta2	string	Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
distributed	string	Whether to use distributer training.
earlyStopping	string	Enable early stopping logic during training.
earlyStoppingDelay	string	Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
earlyStoppingPatience	string	Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
enableOnnxNormalization	string	Enable normalization when exporting ONNX model.
evaluationFrequency	string	Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
gradientAccumulationStep	string	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.
layersToFreeze	string	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: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
learningRate	string	Initial learning rate. Must be a float in the range [0, 1].
learningRateScheduler	string	Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
modelName	string	Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
momentum	string	Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
nesterov	string	Enable nesterov when optimizer is 'sgd'.
numberOfEpochs	string	Number of training epochs. Must be a positive integer.
numberOfWorkers	string	Number of data loader workers. Must be a non-negative integer.
optimizer	string	Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
randomSeed	string	Random seed to be used when using deterministic training.
stepLRGamma	string	Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
stepLRStepSize	string	Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
trainingBatchSize	string	Training batch size. Must be a positive integer.
trainingCropSize	string	Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
validationBatchSize	string	Validation batch size. Must be a positive integer.
validationCropSize	string	Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
validationResizeSize	string	Image size to which to resize before cropping for validation dataset. Must be a positive integer.
warmupCosineLRCycles	string	Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
warmupCosineLRWarmupEpochs	string	Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
weightDecay	string	Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
weightedLoss	string	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.

ImageModelDistributionSettingsObjectDetection

Distribution expressions to sweep over values of model settings. Some examples are:

ModelName = "choice('seresnext', 'resnest50')";
LearningRate = "uniform(0.001, 0.01)";
LayersToFreeze = "choice(0, 2)";
```</example>
For more details on how to compose distribution expressions please check the documentation:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters
For more information on the available settings please visit the official documentation:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.

Name	Type	Description
amsGradient	string	Enable AMSGrad when optimizer is 'adam' or 'adamw'.
augmentations	string	Settings for using Augmentations.
beta1	string	Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
beta2	string	Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
boxDetectionsPerImage	string	Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.
boxScoreThreshold	string	During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].
distributed	string	Whether to use distributer training.
earlyStopping	string	Enable early stopping logic during training.
earlyStoppingDelay	string	Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
earlyStoppingPatience	string	Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
enableOnnxNormalization	string	Enable normalization when exporting ONNX model.
evaluationFrequency	string	Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
gradientAccumulationStep	string	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.
imageSize	string	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.
layersToFreeze	string	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: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
learningRate	string	Initial learning rate. Must be a float in the range [0, 1].
learningRateScheduler	string	Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
maxSize	string	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.
minSize	string	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.
modelName	string	Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
modelSize	string	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.
momentum	string	Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
multiScale	string	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.
nesterov	string	Enable nesterov when optimizer is 'sgd'.
nmsIouThreshold	string	IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1].
numberOfEpochs	string	Number of training epochs. Must be a positive integer.
numberOfWorkers	string	Number of data loader workers. Must be a non-negative integer.
optimizer	string	Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
randomSeed	string	Random seed to be used when using deterministic training.
stepLRGamma	string	Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
stepLRStepSize	string	Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
tileGridSize	string	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.
tileOverlapRatio	string	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.
tilePredictionsNmsThreshold	string	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
trainingBatchSize	string	Training batch size. Must be a positive integer.
validationBatchSize	string	Validation batch size. Must be a positive integer.
validationIouThreshold	string	IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
validationMetricType	string	Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'.
warmupCosineLRCycles	string	Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
warmupCosineLRWarmupEpochs	string	Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
weightDecay	string	Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].

ImageModelSettingsClassification

Settings used for training the model. For more information on the available settings please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.

Name	Type	Default value	Description
advancedSettings	string		Settings for advanced scenarios.
amsGradient	boolean		Enable AMSGrad when optimizer is 'adam' or 'adamw'.
augmentations	string		Settings for using Augmentations.
beta1	number		Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
beta2	number		Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
checkpointFrequency	integer		Frequency to store model checkpoints. Must be a positive integer.
checkpointModel	MLFlowModelJobInput		The pretrained checkpoint model for incremental training.
checkpointRunId	string		The id of a previous run that has a pretrained checkpoint for incremental training.
distributed	boolean		Whether to use distributed training.
earlyStopping	boolean		Enable early stopping logic during training.
earlyStoppingDelay	integer		Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
earlyStoppingPatience	integer		Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
enableOnnxNormalization	boolean		Enable normalization when exporting ONNX model.
evaluationFrequency	integer		Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
gradientAccumulationStep	integer		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.
layersToFreeze	integer		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: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
learningRate	number		Initial learning rate. Must be a float in the range [0, 1].
learningRateScheduler	LearningRateScheduler	None	Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
modelName	string		Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
momentum	number		Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
nesterov	boolean		Enable nesterov when optimizer is 'sgd'.
numberOfEpochs	integer		Number of training epochs. Must be a positive integer.
numberOfWorkers	integer		Number of data loader workers. Must be a non-negative integer.
optimizer	StochasticOptimizer	None	Type of optimizer.
randomSeed	integer		Random seed to be used when using deterministic training.
stepLRGamma	number		Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
stepLRStepSize	integer		Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
trainingBatchSize	integer		Training batch size. Must be a positive integer.
trainingCropSize	integer		Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
validationBatchSize	integer		Validation batch size. Must be a positive integer.
validationCropSize	integer		Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
validationResizeSize	integer		Image size to which to resize before cropping for validation dataset. Must be a positive integer.
warmupCosineLRCycles	number		Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
warmupCosineLRWarmupEpochs	integer		Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
weightDecay	number		Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
weightedLoss	integer		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.

ImageModelSettingsObjectDetection

Settings used for training the model. For more information on the available settings please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.

Name	Type	Default value	Description
advancedSettings	string		Settings for advanced scenarios.
amsGradient	boolean		Enable AMSGrad when optimizer is 'adam' or 'adamw'.
augmentations	string		Settings for using Augmentations.
beta1	number		Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
beta2	number		Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
boxDetectionsPerImage	integer		Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.
boxScoreThreshold	number		During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].
checkpointFrequency	integer		Frequency to store model checkpoints. Must be a positive integer.
checkpointModel	MLFlowModelJobInput		The pretrained checkpoint model for incremental training.
checkpointRunId	string		The id of a previous run that has a pretrained checkpoint for incremental training.
distributed	boolean		Whether to use distributed training.
earlyStopping	boolean		Enable early stopping logic during training.
earlyStoppingDelay	integer		Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
earlyStoppingPatience	integer		Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
enableOnnxNormalization	boolean		Enable normalization when exporting ONNX model.
evaluationFrequency	integer		Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
gradientAccumulationStep	integer		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.
imageSize	integer		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.
layersToFreeze	integer		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: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
learningRate	number		Initial learning rate. Must be a float in the range [0, 1].
learningRateScheduler	LearningRateScheduler	None	Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
maxSize	integer		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.
minSize	integer		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.
modelName	string		Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
modelSize	ModelSize	None	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.
momentum	number		Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
multiScale	boolean		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.
nesterov	boolean		Enable nesterov when optimizer is 'sgd'.
nmsIouThreshold	number		IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1].
numberOfEpochs	integer		Number of training epochs. Must be a positive integer.
numberOfWorkers	integer		Number of data loader workers. Must be a non-negative integer.
optimizer	StochasticOptimizer	None	Type of optimizer.
randomSeed	integer		Random seed to be used when using deterministic training.
stepLRGamma	number		Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
stepLRStepSize	integer		Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
tileGridSize	string		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.
tileOverlapRatio	number		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.
tilePredictionsNmsThreshold	number		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.
trainingBatchSize	integer		Training batch size. Must be a positive integer.
validationBatchSize	integer		Validation batch size. Must be a positive integer.
validationIouThreshold	number		IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
validationMetricType	ValidationMetricType	None	Metric computation method to use for validation metrics.
warmupCosineLRCycles	number		Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
warmupCosineLRWarmupEpochs	integer		Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
weightDecay	number		Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].

ImageObjectDetection

Image Object Detection. Object detection is used to identify objects in an image and locate each object with a bounding box e.g. locate all dogs and cats in an image and draw a bounding box around each.

Name	Type	Default value	Description
limitSettings	ImageLimitSettings		[Required] Limit settings for the AutoML job.
logVerbosity	LogVerbosity	Info	Log verbosity for the job.
modelSettings	ImageModelSettingsObjectDetection		Settings used for training the model.
primaryMetric	ObjectDetectionPrimaryMetrics	MeanAveragePrecision	Primary metric to optimize for this task.
searchSpace	ImageModelDistributionSettingsObjectDetection[]		Search space for sampling different combinations of models and their hyperparameters.
sweepSettings	ImageSweepSettings		Model sweeping and hyperparameter sweeping related settings.
targetColumnName	string		Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
taskType	string: ImageObjectDetection		[Required] Task type for AutoMLJob.
trainingData	MLTableJobInput		[Required] Training data input.
validationData	MLTableJobInput		Validation data inputs.
validationDataSize	number		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.

ImageSweepSettings

Model sweeping and hyperparameter sweeping related settings.

Name	Type	Description
earlyTermination	EarlyTerminationPolicy: BanditPolicy MedianStoppingPolicy TruncationSelectionPolicy	Type of early termination policy.
samplingAlgorithm	SamplingAlgorithmType	[Required] Type of the hyperparameter sampling algorithms.

InputDeliveryMode

Enum to determine the input data delivery mode.

Name	Type	Description
Direct	string
Download	string
EvalDownload	string
EvalMount	string
ReadOnlyMount	string
ReadWriteMount	string

InstanceSegmentationPrimaryMetrics

Primary metrics for InstanceSegmentation tasks.

Name	Type	Description
MeanAveragePrecision	string	Mean Average Precision (MAP) is the average of AP (Average Precision). AP is calculated for each class and averaged to get the MAP.

JobBaseResource

Azure Resource Manager resource envelope.

Name	Type	Description
id	string	Fully qualified resource ID for the resource. Ex - /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}
name	string	The name of the resource
properties	JobBase: AutoMLJob CommandJob PipelineJob SweepJob	[Required] Additional attributes of the entity.
systemData	systemData	Azure Resource Manager metadata containing createdBy and modifiedBy information.
type	string	The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or "Microsoft.Storage/storageAccounts"

JobInputType

Enum to determine the Job Input Type.

Name	Type	Description
custom_model	string
literal	string
mlflow_model	string
mltable	string
triton_model	string
uri_file	string
uri_folder	string

JobLimitsType

Name	Type	Description
Command	string
Sweep	string

JobOutputType

Enum to determine the Job Output Type.

Name	Type	Description
custom_model	string
mlflow_model	string
mltable	string
triton_model	string
uri_file	string
uri_folder	string

JobResourceConfiguration

Name	Type	Default value	Description
dockerArgs	string		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.
instanceCount	integer	1	Optional number of instances or nodes used by the compute target.
instanceType	string		Optional type of VM used as supported by the compute target.
properties	object		Additional properties bag.
shmSize	string	2g	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).

JobService

Job endpoint definition

Name	Type	Description
endpoint	string	Url for endpoint.
errorMessage	string	Any error in the service.
jobServiceType	string	Endpoint type.
nodes	Nodes: AllNodes	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.
port	integer	Port for endpoint.
properties	object	Additional properties to set on the endpoint.
status	string	Status of endpoint.

JobStatus

The status of a job.

Name	Type	Description
CancelRequested	string	Cancellation has been requested for the job.
Canceled	string	Following cancellation request, the job is now successfully canceled.
Completed	string	Job completed successfully. This reflects that both the job itself and output collection states completed successfully
Failed	string	Job failed.
Finalizing	string	Job is completed in the target. It is in output collection state now.
NotResponding	string	When heartbeat is enabled, if the run isn't updating any information to RunHistory then the run goes to NotResponding state. NotResponding is the only state that is exempt from strict transition orders. A run can go from NotResponding to any of the previous states.
NotStarted	string	Run hasn't started yet.
Paused	string	The job is paused by users. Some adjustment to labeling jobs can be made only in paused state.
Preparing	string	The run environment is being prepared.
Provisioning	string	(Not used currently) It will be used if ES is creating the compute target.
Queued	string	The job is queued in the compute target. For example, in BatchAI the job is in queued state, while waiting for all required nodes to be ready.
Running	string	The job started to run in the compute target.
Starting	string	Run has started. The user has a run ID.
Unknown	string	Default job status if not mapped to all other statuses

JobTier

Enum to determine the job tier.

Name	Type	Description
Basic	string
Null	string
Premium	string
Spot	string
Standard	string

JobType

Enum to determine the type of job.

Name	Type	Description
AutoML	string
Command	string
Pipeline	string
Sweep	string

LearningRateScheduler

Learning rate scheduler enum.

Name	Type	Description
None	string	No learning rate scheduler selected.
Step	string	Step learning rate scheduler.
WarmupCosine	string	Cosine Annealing With Warmup.

LiteralJobInput

Literal input type.

Name	Type	Description
description	string	Description for the input.
jobInputType	string: literal	[Required] Specifies the type of job.
value	string	[Required] Literal value for the input.

LogVerbosity

Enum for setting log verbosity.

Name	Type	Description
Critical	string	Only critical statements logged.
Debug	string	Debug and above log statements logged.
Error	string	Error and above log statements logged.
Info	string	Info and above log statements logged.
NotSet	string	No logs emitted.
Warning	string	Warning and above log statements logged.

ManagedIdentity

Managed identity configuration.

Name	Type	Description
clientId	string	Specifies a user-assigned identity by client ID. For system-assigned, do not set this field.
identityType	string: Managed	[Required] Specifies the type of identity framework.
objectId	string	Specifies a user-assigned identity by object ID. For system-assigned, do not set this field.
resourceId	string	Specifies a user-assigned identity by ARM resource ID. For system-assigned, do not set this field.

MedianStoppingPolicy

Defines an early termination policy based on running averages of the primary metric of all runs

Name	Type	Default value	Description
delayEvaluation	integer	0	Number of intervals by which to delay the first evaluation.
evaluationInterval	integer	0	Interval (number of runs) between policy evaluations.
policyType	string: MedianStopping		[Required] Name of policy configuration

MLFlowModelJobInput

Name	Type	Default value	Description
description	string		Description for the input.
jobInputType	string: mlflow_model		[Required] Specifies the type of job.
mode	InputDeliveryMode	ReadOnlyMount	Input Asset Delivery Mode.
uri	string		[Required] Input Asset URI.

MLFlowModelJobOutput

Name	Type	Default value	Description
description	string		Description for the output.
jobOutputType	string: mlflow_model		[Required] Specifies the type of job.
mode	OutputDeliveryMode	ReadWriteMount	Output Asset Delivery Mode.
uri	string		Output Asset URI.

MLTableJobInput

Name	Type	Default value	Description
description	string		Description for the input.
jobInputType	string: mltable		[Required] Specifies the type of job.
mode	InputDeliveryMode	ReadOnlyMount	Input Asset Delivery Mode.
uri	string		[Required] Input Asset URI.

MLTableJobOutput

Name	Type	Default value	Description
description	string		Description for the output.
jobOutputType	string: mltable		[Required] Specifies the type of job.
mode	OutputDeliveryMode	ReadWriteMount	Output Asset Delivery Mode.
uri	string		Output Asset URI.

ModelSize

Image model size.

Name	Type	Description
ExtraLarge	string	Extra large size.
Large	string	Large size.
Medium	string	Medium size.
None	string	No value selected.
Small	string	Small size.

Mpi

MPI distribution configuration.

Name	Type	Description
distributionType	string: Mpi	[Required] Specifies the type of distribution framework.
processCountPerInstance	integer	Number of processes per MPI node.

NCrossValidationsMode

Determines how N-Cross validations value is determined.

Name	Type	Description
Auto	string	Determine N-Cross validations value automatically. Supported only for 'Forecasting' AutoML task.
Custom	string	Use custom N-Cross validations value.

NlpVerticalFeaturizationSettings

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

NlpVerticalLimitSettings

Job execution constraints.

Name	Type	Default value	Description
maxConcurrentTrials	integer	1	Maximum Concurrent AutoML iterations.
maxTrials	integer	1	Number of AutoML iterations.
timeout	string	P7D	AutoML job timeout.

NodesValueType

The enumerated types for the nodes value

Name	Type	Description
All	string

ObjectDetectionPrimaryMetrics

Primary metrics for Image ObjectDetection task.

Name	Type	Description
MeanAveragePrecision	string	Mean Average Precision (MAP) is the average of AP (Average Precision). AP is calculated for each class and averaged to get the MAP.

Objective

Optimization objective.

Name	Type	Description
goal	Goal	[Required] Defines supported metric goals for hyperparameter tuning
primaryMetric	string	[Required] Name of the metric to optimize.

OutputDeliveryMode

Output data delivery mode enums.

Name	Type	Description
ReadWriteMount	string
Upload	string

PipelineJob

Pipeline Job definition: defines generic to MFE attributes.

Name	Type	Default value	Description
componentId	string		ARM resource ID of the component resource.
computeId	string		ARM resource ID of the compute resource.
description	string		The asset description text.
displayName	string		Display name of job.
experimentName	string	Default	The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
identity	IdentityConfiguration: AmlToken ManagedIdentity UserIdentity		Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
inputs	object		Inputs for the pipeline job.
isArchived	boolean	False	Is the asset archived?
jobType	string: Pipeline		[Required] Specifies the type of job.
jobs	object		Jobs construct the Pipeline Job.
outputs	object		Outputs for the pipeline job
properties	object		The asset property dictionary.
services	<string,  JobService>		List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
settings	object		Pipeline settings, for things like ContinueRunOnStepFailure etc.
sourceJobId	string		ARM resource ID of source job.
status	JobStatus		Status of the job.
tags	object		Tag dictionary. Tags can be added, removed, and updated.

PyTorch

PyTorch distribution configuration.

Name	Type	Description
distributionType	string: PyTorch	[Required] Specifies the type of distribution framework.
processCountPerInstance	integer	Number of processes per node.

QueueSettings

Name	Type	Default value	Description
jobTier	JobTier	Null	Controls the compute job tier

RandomSamplingAlgorithm

Defines a Sampling Algorithm that generates values randomly

Name	Type	Default value	Description
rule	RandomSamplingAlgorithmRule	Random	The specific type of random algorithm
samplingAlgorithmType	string: Random		[Required] The algorithm used for generating hyperparameter values, along with configuration properties
seed	integer		An optional integer to use as the seed for random number generation

RandomSamplingAlgorithmRule

The specific type of random algorithm

Name	Type	Description
Random	string
Sobol	string

Regression

Regression task in AutoML Table vertical.

Name	Type	Default value	Description
cvSplitColumnNames	string[]		Columns to use for CVSplit data.
featurizationSettings	TableVerticalFeaturizationSettings		Featurization inputs needed for AutoML job.
limitSettings	TableVerticalLimitSettings		Execution constraints for AutoMLJob.
logVerbosity	LogVerbosity	Info	Log verbosity for the job.
nCrossValidations	NCrossValidations: AutoNCrossValidations CustomNCrossValidations		Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
primaryMetric	RegressionPrimaryMetrics	NormalizedRootMeanSquaredError	Primary metric for regression task.
targetColumnName	string		Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
taskType	string: Regression		[Required] Task type for AutoMLJob.
testData	MLTableJobInput		Test data input.
testDataSize	number		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.
trainingData	MLTableJobInput		[Required] Training data input.
trainingSettings	RegressionTrainingSettings		Inputs for training phase for an AutoML Job.
validationData	MLTableJobInput		Validation data inputs.
validationDataSize	number		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.
weightColumnName	string		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.

RegressionModels

Enum for all Regression models supported by AutoML.

Name	Type	Description
DecisionTree	string	Decision Trees are a non-parametric supervised learning method used for both classification and regression tasks. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.
ElasticNet	string	Elastic net is a popular type of regularized linear regression that combines two popular penalties, specifically the L1 and L2 penalty functions.
ExtremeRandomTrees	string	Extreme Trees is an ensemble machine learning algorithm that combines the predictions from many decision trees. It is related to the widely used random forest algorithm.
GradientBoosting	string	The technique of transiting week learners into a strong learner is called Boosting. The gradient boosting algorithm process works on this theory of execution.
KNN	string	K-nearest neighbors (KNN) algorithm uses 'feature similarity' to predict the values of new datapoints which further means that the new data point will be assigned a value based on how closely it matches the points in the training set.
LassoLars	string	Lasso model fit with Least Angle Regression a.k.a. Lars. It is a Linear Model trained with an L1 prior as regularizer.
LightGBM	string	LightGBM is a gradient boosting framework that uses tree based learning algorithms.
RandomForest	string	Random forest is a supervised learning algorithm. The "forest" it builds, is an ensemble of decision trees, usually trained with the “bagging” method. The general idea of the bagging method is that a combination of learning models increases the overall result.
SGD	string	SGD: Stochastic gradient descent is an optimization algorithm often used in machine learning applications to find the model parameters that correspond to the best fit between predicted and actual outputs. It's an inexact but powerful technique.
XGBoostRegressor	string	XGBoostRegressor: Extreme Gradient Boosting Regressor is a supervised machine learning model using ensemble of base learners.

RegressionPrimaryMetrics

Primary metrics for Regression task.

Name	Type	Description
NormalizedMeanAbsoluteError	string	The Normalized Mean Absolute Error (NMAE) is a validation metric to compare the Mean Absolute Error (MAE) of (time) series with different scales.
NormalizedRootMeanSquaredError	string	The Normalized Root Mean Squared Error (NRMSE) the RMSE facilitates the comparison between models with different scales.
R2Score	string	The R2 score is one of the performance evaluation measures for forecasting-based machine learning models.
SpearmanCorrelation	string	The Spearman's rank coefficient of correlation is a nonparametric measure of rank correlation.

RegressionTrainingSettings

Regression Training related configuration.

Name	Type	Default value	Description
allowedTrainingAlgorithms	RegressionModels[]		Allowed models for regression task.
blockedTrainingAlgorithms	RegressionModels[]		Blocked models for regression task.
enableDnnTraining	boolean	False	Enable recommendation of DNN models.
enableModelExplainability	boolean	True	Flag to turn on explainability on best model.
enableOnnxCompatibleModels	boolean	False	Flag for enabling onnx compatible models.
enableStackEnsemble	boolean	True	Enable stack ensemble run.
enableVoteEnsemble	boolean	True	Enable voting ensemble run.
ensembleModelDownloadTimeout	string	PT5M	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.
stackEnsembleSettings	StackEnsembleSettings		Stack ensemble settings for stack ensemble run.

SamplingAlgorithmType

Name	Type	Description
Bayesian	string
Grid	string
Random	string

SeasonalityMode

Forecasting seasonality mode.

Name	Type	Description
Auto	string	Seasonality to be determined automatically.
Custom	string	Use the custom seasonality value.

ShortSeriesHandlingConfiguration

The parameter defining how if AutoML should handle short time series.

Name	Type	Description
Auto	string	Short series will be padded if there are no long series, otherwise short series will be dropped.
Drop	string	All the short series will be dropped.
None	string	Represents no/null value.
Pad	string	All the short series will be padded.

StackEnsembleSettings

Advances setting to customize StackEnsemble run.

Name	Type	Default value	Description
stackMetaLearnerKWargs	object		Optional parameters to pass to the initializer of the meta-learner.
stackMetaLearnerTrainPercentage	number	0.2	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.
stackMetaLearnerType	StackMetaLearnerType	None	The meta-learner is a model trained on the output of the individual heterogeneous models.

StackMetaLearnerType

The meta-learner is a model trained on the output of the individual heterogeneous models. Default meta-learners are LogisticRegression for classification tasks (or LogisticRegressionCV if cross-validation is enabled) and ElasticNet for regression/forecasting tasks (or ElasticNetCV if cross-validation is enabled). This parameter can be one of the following strings: LogisticRegression, LogisticRegressionCV, LightGBMClassifier, ElasticNet, ElasticNetCV, LightGBMRegressor, or LinearRegression

Name	Type	Description
ElasticNet	string	Default meta-learners are LogisticRegression for regression task.
ElasticNetCV	string	Default meta-learners are LogisticRegression for regression task when CV is on.
LightGBMClassifier	string
LightGBMRegressor	string
LinearRegression	string
LogisticRegression	string	Default meta-learners are LogisticRegression for classification tasks.
LogisticRegressionCV	string	Default meta-learners are LogisticRegression for classification task when CV is on.
None	string

StochasticOptimizer

Stochastic optimizer for image models.

Name	Type	Description
Adam	string	Adam is algorithm the optimizes stochastic objective functions based on adaptive estimates of moments
Adamw	string	AdamW is a variant of the optimizer Adam that has an improved implementation of weight decay.
None	string	No optimizer selected.
Sgd	string	Stochastic Gradient Descent optimizer.

SweepJob

Sweep job definition.

Name	Type	Default value	Description
componentId	string		ARM resource ID of the component resource.
computeId	string		ARM resource ID of the compute resource.
description	string		The asset description text.
displayName	string		Display name of job.
earlyTermination	EarlyTerminationPolicy: BanditPolicy MedianStoppingPolicy TruncationSelectionPolicy		Early termination policies enable canceling poor-performing runs before they complete
experimentName	string	Default	The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
identity	IdentityConfiguration: AmlToken ManagedIdentity UserIdentity		Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
inputs	object		Mapping of input data bindings used in the job.
isArchived	boolean	False	Is the asset archived?
jobType	string: Sweep		[Required] Specifies the type of job.
limits	SweepJobLimits	{}	Sweep Job limit.
objective	Objective		[Required] Optimization objective.
outputs	object		Mapping of output data bindings used in the job.
properties	object		The asset property dictionary.
queueSettings	QueueSettings		Queue settings for the job
samplingAlgorithm	SamplingAlgorithm: BayesianSamplingAlgorithm GridSamplingAlgorithm RandomSamplingAlgorithm		[Required] The hyperparameter sampling algorithm
searchSpace	object		[Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
services	<string,  JobService>		List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
status	JobStatus		Status of the job.
tags	object		Tag dictionary. Tags can be added, removed, and updated.
trial	TrialComponent		[Required] Trial component definition.

SweepJobLimits

Sweep Job limit class.

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

systemData

Metadata pertaining to creation and last modification of the resource.

Name	Type	Description
createdAt	string	The timestamp of resource creation (UTC).
createdBy	string	The identity that created the resource.
createdByType	createdByType	The type of identity that created the resource.
lastModifiedAt	string	The timestamp of resource last modification (UTC)
lastModifiedBy	string	The identity that last modified the resource.
lastModifiedByType	createdByType	The type of identity that last modified the resource.

TableVerticalFeaturizationSettings

Featurization Configuration.

Name	Type	Default value	Description
blockedTransformers	BlockedTransformers[]		These transformers shall not be used in featurization.
columnNameAndTypes	object		Dictionary of column name and its type (int, float, string, datetime etc).
datasetLanguage	string		Dataset language, useful for the text data.
enableDnnFeaturization	boolean	False	Determines whether to use Dnn based featurizers for data featurization.
mode	FeaturizationMode	Auto	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.
transformerParams	object		User can specify additional transformers to be used along with the columns to which it would be applied and parameters for the transformer constructor.

TableVerticalLimitSettings

Job execution constraints.

Name	Type	Default value	Description
enableEarlyTermination	boolean	True	Enable early termination, determines whether or not if AutoMLJob will terminate early if there is no score improvement in last 20 iterations.
exitScore	number		Exit score for the AutoML job.
maxConcurrentTrials	integer	1	Maximum Concurrent iterations.
maxCoresPerTrial	integer	-1	Max cores per iteration.
maxTrials	integer	1000	Number of iterations.
timeout	string	PT6H	AutoML job timeout.
trialTimeout	string	PT30M	Iteration timeout.

TargetAggregationFunction

Target aggregate function.

Name	Type	Description
Max	string
Mean	string
Min	string
None	string	Represent no value set.
Sum	string

TargetLagsMode

Target lags selection modes.

Name	Type	Description
Auto	string	Target lags to be determined automatically.
Custom	string	Use the custom target lags.

TargetRollingWindowSizeMode

Target rolling windows size mode.

Name	Type	Description
Auto	string	Determine rolling windows size automatically.
Custom	string	Use the specified rolling window size.

TaskType

AutoMLJob Task type.

Name	Type	Description
Classification	string	Classification in machine learning and statistics is a supervised learning approach in which the computer program learns from the data given to it and make new observations or classifications.
Forecasting	string	Forecasting is a special kind of regression task that deals with time-series data and creates forecasting model that can be used to predict the near future values based on the inputs.
ImageClassification	string	Image Classification. Multi-class image classification is used when an image is classified with only a single label from a set of classes - e.g. each image is classified as either an image of a 'cat' or a 'dog' or a 'duck'.
ImageClassificationMultilabel	string	Image Classification Multilabel. Multi-label image classification is used when an image could have one or more labels from a set of labels - e.g. an image could be labeled with both 'cat' and 'dog'.
ImageInstanceSegmentation	string	Image Instance Segmentation. Instance segmentation is used to identify objects in an image at the pixel level, drawing a polygon around each object in the image.
ImageObjectDetection	string	Image Object Detection. Object detection is used to identify objects in an image and locate each object with a bounding box e.g. locate all dogs and cats in an image and draw a bounding box around each.
Regression	string	Regression means to predict the value using the input data. Regression models are used to predict a continuous value.
TextClassification	string	Text classification (also known as text tagging or text categorization) is the process of sorting texts into categories. Categories are mutually exclusive.
TextClassificationMultilabel	string	Multilabel classification task assigns each sample to a group (zero or more) of target labels.
TextNER	string	Text Named Entity Recognition a.k.a. TextNER. Named Entity Recognition (NER) is the ability to take free-form text and identify the occurrences of entities such as people, locations, organizations, and more.

TensorFlow

TensorFlow distribution configuration.

Name	Type	Default value	Description
distributionType	string: TensorFlow		[Required] Specifies the type of distribution framework.
parameterServerCount	integer	0	Number of parameter server tasks.
workerCount	integer		Number of workers. If not specified, will default to the instance count.

TextClassification

Text Classification task in AutoML NLP vertical. NLP - Natural Language Processing.

Name	Type	Default value	Description
featurizationSettings	NlpVerticalFeaturizationSettings		Featurization inputs needed for AutoML job.
limitSettings	NlpVerticalLimitSettings		Execution constraints for AutoMLJob.
logVerbosity	LogVerbosity	Info	Log verbosity for the job.
primaryMetric	ClassificationPrimaryMetrics	Accuracy	Primary metric for Text-Classification task.
targetColumnName	string		Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
taskType	string: TextClassification		[Required] Task type for AutoMLJob.
trainingData	MLTableJobInput		[Required] Training data input.
validationData	MLTableJobInput		Validation data inputs.

TextClassificationMultilabel

Text Classification Multilabel task in AutoML NLP vertical. NLP - Natural Language Processing.

Name	Type	Default value	Description
featurizationSettings	NlpVerticalFeaturizationSettings		Featurization inputs needed for AutoML job.
limitSettings	NlpVerticalLimitSettings		Execution constraints for AutoMLJob.
logVerbosity	LogVerbosity	Info	Log verbosity for the job.
primaryMetric	ClassificationMultilabelPrimaryMetrics		Primary metric for Text-Classification-Multilabel task. Currently only Accuracy is supported as primary metric, hence user need not set it explicitly.
targetColumnName	string		Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
taskType	string: TextClassificationMultilabel		[Required] Task type for AutoMLJob.
trainingData	MLTableJobInput		[Required] Training data input.
validationData	MLTableJobInput		Validation data inputs.

TextNer

Text-NER task in AutoML NLP vertical. NER - Named Entity Recognition. NLP - Natural Language Processing.

Name	Type	Default value	Description
featurizationSettings	NlpVerticalFeaturizationSettings		Featurization inputs needed for AutoML job.
limitSettings	NlpVerticalLimitSettings		Execution constraints for AutoMLJob.
logVerbosity	LogVerbosity	Info	Log verbosity for the job.
primaryMetric	ClassificationPrimaryMetrics		Primary metric for Text-NER task. Only 'Accuracy' is supported for Text-NER, so user need not set this explicitly.
targetColumnName	string		Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
taskType	string: TextNER		[Required] Task type for AutoMLJob.
trainingData	MLTableJobInput		[Required] Training data input.
validationData	MLTableJobInput		Validation data inputs.

TrialComponent

Trial component definition.

Name	Type	Default value	Description
codeId	string		ARM resource ID of the code asset.
command	string		[Required] The command to execute on startup of the job. eg. "python train.py"
distribution	DistributionConfiguration: Mpi PyTorch TensorFlow		Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
environmentId	string		[Required] The ARM resource ID of the Environment specification for the job.
environmentVariables	object		Environment variables included in the job.
resources	JobResourceConfiguration	{}	Compute Resource configuration for the job.

TritonModelJobInput

Name	Type	Default value	Description
description	string		Description for the input.
jobInputType	string: triton_model		[Required] Specifies the type of job.
mode	InputDeliveryMode	ReadOnlyMount	Input Asset Delivery Mode.
uri	string		[Required] Input Asset URI.

TritonModelJobOutput

Name	Type	Default value	Description
description	string		Description for the output.
jobOutputType	string: triton_model		[Required] Specifies the type of job.
mode	OutputDeliveryMode	ReadWriteMount	Output Asset Delivery Mode.
uri	string		Output Asset URI.

TruncationSelectionPolicy

Defines an early termination policy that cancels a given percentage of runs at each evaluation interval.

Name	Type	Default value	Description
delayEvaluation	integer	0	Number of intervals by which to delay the first evaluation.
evaluationInterval	integer	0	Interval (number of runs) between policy evaluations.
policyType	string: TruncationSelection		[Required] Name of policy configuration
truncationPercentage	integer	0	The percentage of runs to cancel at each evaluation interval.

UriFileJobInput

Name	Type	Default value	Description
description	string		Description for the input.
jobInputType	string: uri_file		[Required] Specifies the type of job.
mode	InputDeliveryMode	ReadOnlyMount	Input Asset Delivery Mode.
uri	string		[Required] Input Asset URI.

UriFileJobOutput

Name	Type	Default value	Description
description	string		Description for the output.
jobOutputType	string: uri_file		[Required] Specifies the type of job.
mode	OutputDeliveryMode	ReadWriteMount	Output Asset Delivery Mode.
uri	string		Output Asset URI.

UriFolderJobInput

Name	Type	Default value	Description
description	string		Description for the input.
jobInputType	string: uri_folder		[Required] Specifies the type of job.
mode	InputDeliveryMode	ReadOnlyMount	Input Asset Delivery Mode.
uri	string		[Required] Input Asset URI.

UriFolderJobOutput

Name	Type	Default value	Description
description	string		Description for the output.
jobOutputType	string: uri_folder		[Required] Specifies the type of job.
mode	OutputDeliveryMode	ReadWriteMount	Output Asset Delivery Mode.
uri	string		Output Asset URI.

UserIdentity

User identity configuration.

Name	Type	Description
identityType	string: UserIdentity	[Required] Specifies the type of identity framework.

UseStl

Configure STL Decomposition of the time-series target column.

Name	Type	Description
None	string	No stl decomposition.
Season	string
SeasonTrend	string

ValidationMetricType

Metric computation method to use for validation metrics in image tasks.

Name	Type	Description
Coco	string	Coco metric.
CocoVoc	string	CocoVoc metric.
None	string	No metric.
Voc	string	Voc metric.