Track Azure Synapse Analytics ML experiments with MLflow and Azure Machine Learning

In this article, learn how to enable MLflow to connect to Azure Machine Learning while working in an Azure Synapse Analytics workspace. You can leverage this configuration for tracking, model management and model deployment.

MLflow is an open-source library for managing the life cycle of your machine learning experiments. MLFlow Tracking is a component of MLflow that logs and tracks your training run metrics and model artifacts. Learn more about MLflow.

If you have an MLflow Project to train with Azure Machine Learning, see Train ML models with MLflow Projects and Azure Machine Learning (preview).

Prerequisites

Install libraries

To install libraries on your dedicated cluster in Azure Synapse Analytics:

  1. Create a requirements.txt file with the packages your experiments requires, but making sure it also includes the following packages:

    requirements.txt

    mlflow
    azureml-mlflow
    azure-ai-ml
    
  2. Navigate to Azure Analytics Workspace portal.

  3. Navigate to the Manage tab and select Apache Spark Pools.

  4. Click the three dots next to the cluster name, and select Packages.

    install mlflow packages in Azure Synapse Analytics

  5. On the Requirements files section, click on Upload.

  6. Upload the requirements.txt file.

  7. Wait for your cluster to restart.

Track experiments with MLflow

Azure Synapse Analytics can be configured to track experiments using MLflow to Azure Machine Learning workspace. Azure Machine Learning provides a centralized repository to manage the entire lifecycle of experiments, models and deployments. It also has the advantage of enabling easier path to deployment using Azure Machine Learning deployment options.

Configuring your notebooks to use MLflow connected to Azure Machine Learning

To use Azure Machine Learning as your centralized repository for experiments, you can leverage MLflow. On each notebook where you are working on, you have to configure the tracking URI to point to the workspace you will be using. The following example shows how it can be done:

Configure tracking URI

  1. Get the tracking URI for your workspace:

    APPLIES TO: Azure CLI ml extension v2 (current)

    1. Login and configure your workspace:

      az account set --subscription <subscription>
      az configure --defaults workspace=<workspace> group=<resource-group> location=<location> 
      
    2. You can get the tracking URI using the az ml workspace command:

      az ml workspace show --query mlflow_tracking_uri
      
  2. Configuring the tracking URI:

    Then the method set_tracking_uri() points the MLflow tracking URI to that URI.

    import mlflow
    
    mlflow.set_tracking_uri(mlflow_tracking_uri)
    

    Tip

    When working on shared environments, like an Azure Databricks cluster, Azure Synapse Analytics cluster, or similar, it is useful to set the environment variable MLFLOW_TRACKING_URI at the cluster level to automatically configure the MLflow tracking URI to point to Azure Machine Learning for all the sessions running in the cluster rather than to do it on a per-session basis.

Configure authentication

Once the tracking is configured, you'll also need to configure how the authentication needs to happen to the associated workspace. By default, the Azure Machine Learning plugin for MLflow will perform interactive authentication by opening the default browser to prompt for credentials. Refer to Configure MLflow for Azure Machine Learning: Configure authentication to additional ways to configure authentication for MLflow in Azure Machine Learning workspaces.

For interactive jobs where there's a user connected to the session, you can rely on Interactive Authentication and hence no further action is required.

Warning

Interactive browser authentication will block code execution when prompting for credentials. It is not a suitable option for authentication in unattended environments like training jobs. We recommend to configure other authentication mode.

For those scenarios where unattended execution is required, you'll have to configure a service principal to communicate with Azure Machine Learning.

import os

os.environ["AZURE_TENANT_ID"] = "<AZURE_TENANT_ID>"
os.environ["AZURE_CLIENT_ID"] = "<AZURE_CLIENT_ID>"
os.environ["AZURE_CLIENT_SECRET"] = "<AZURE_CLIENT_SECRET>"

Tip

When working on shared environments, it is advisable to configure these environment variables at the compute. As a best practice, manage them as secrets in an instance of Azure Key Vault whenever possible. For instance, in Azure Databricks you can use secrets in environment variables as follows in the cluster configuration: AZURE_CLIENT_SECRET={{secrets/<scope-name>/<secret-name>}}. See Reference a secret in an environment variable for how to do it in Azure Databricks or refer to similar documentation in your platform.

Experiment's names in Azure Machine Learning

By default, Azure Machine Learning tracks runs in a default experiment called Default. It is usually a good idea to set the experiment you will be going to work on. Use the following syntax to set the experiment's name:

mlflow.set_experiment(experiment_name="experiment-name")

Tracking parameters, metrics and artifacts

You can use then MLflow in Azure Synapse Analytics in the same way as you're used to. For details see Log & view metrics and log files.

Registering models in the registry with MLflow

Models can be registered in Azure Machine Learning workspace, which offers a centralized repository to manage their lifecycle. The following example logs a model trained with Spark MLLib and also registers it in the registry.

mlflow.spark.log_model(model, 
                       artifact_path = "model", 
                       registered_model_name = "model_name")  
  • If a registered model with the name doesn’t exist, the method registers a new model, creates version 1, and returns a ModelVersion MLflow object.

  • If a registered model with the name already exists, the method creates a new model version and returns the version object.

You can manage models registered in Azure Machine Learning using MLflow. View Manage models registries in Azure Machine Learning with MLflow for more details.

Deploying and consuming models registered in Azure Machine Learning

Models registered in Azure Machine Learning Service using MLflow can be consumed as:

  • An Azure Machine Learning endpoint (real-time and batch): This deployment allows you to leverage Azure Machine Learning deployment capabilities for both real-time and batch inference in Azure Container Instances (ACI), Azure Kubernetes (AKS) or our Managed Endpoints.

  • MLFlow model objects or Pandas UDFs, which can be used in Azure Synapse Analytics notebooks in streaming or batch pipelines.

Deploy models to Azure Machine Learning endpoints

You can leverage the azureml-mlflow plugin to deploy a model to your Azure Machine Learning workspace. Check How to deploy MLflow models page for a complete detail about how to deploy models to the different targets.

Important

Models need to be registered in Azure Machine Learning registry in order to deploy them. Deployment of unregistered models is not supported in Azure Machine Learning.

Deploy models for batch scoring using UDFs

You can choose Azure Synapse Analytics clusters for batch scoring. The MLFlow model is loaded and used as a Spark Pandas UDF to score new data.

from pyspark.sql.types import ArrayType, FloatType 

model_uri = "runs:/"+last_run_id+ {model_path} 

#Create a Spark UDF for the MLFlow model 
pyfunc_udf = mlflow.pyfunc.spark_udf(spark, model_uri) 

#Load Scoring Data into Spark Dataframe 
scoreDf = spark.table({table_name}).where({required_conditions}) 

#Make Prediction 
preds = (scoreDf 
           .withColumn('target_column_name', pyfunc_udf('Input_column1', 'Input_column2', ' Input_column3', …)) 
        ) 

display(preds) 

Clean up resources

If you wish to keep your Azure Synapse Analytics workspace, but no longer need the Azure ML workspace, you can delete the Azure ML workspace. If you don't plan to use the logged metrics and artifacts in your workspace, the ability to delete them individually is unavailable at this time. Instead, delete the resource group that contains the storage account and workspace, so you don't incur any charges:

  1. In the Azure portal, select Resource groups on the far left.

    Delete in the Azure portal

  2. From the list, select the resource group you created.

  3. Select Delete resource group.

  4. Enter the resource group name. Then select Delete.

Next steps