1. Automated Machine Learning Workflows with Databricks Jobs


    To automate machine learning workflows using Databricks on Azure, you'll need to create and configure a Databricks Workspace and then set up Databricks Jobs within that workspace. Databricks Jobs can run notebooks, JARs, Python scripts, or compile code against a cluster. We'll create a Databricks Workspace and configure a Databricks Job to run a machine learning notebook.

    First, you'll require an Azure subscription and the necessary permissions to create and manage Azure resources. You also need to have the Pulumi CLI installed and set up with your Azure credentials.

    Here's how you can automate machine learning workflows with Databricks Jobs:

    1. Set Up the Databricks Workspace: You'll need to create a Databricks Workspace in your Azure environment where you can run your machine learning workflows.

    2. Define a Databricks Cluster: For the Databricks Job to run, it needs a compute cluster. You can either define a new cluster for each job or use an existing interactive or automated cluster.

    3. Configure the Databricks Job: You'll create a Databricks Job resource that references the cluster and provides the details about the notebook, script, or compiled code that the job will execute.

    4. Schedule or Trigger the Job: The Databricks Job resource allows you to define schedules for running your machine learning workflows or set up event triggers.

    Below is a Pulumi program in Python that demonstrates how to create a Databricks Workspace and then set up a job to run a machine learning notebook:

    import pulumi import pulumi_azure as azure # A Databricks Workspace holds all your jobs, notebooks, and other Databricks assets. # It allows you to manage the whole lifecycle of your machine learning workflow. workspace = azure.databricks.Workspace("ml-workspace", resource_group_name="resource-group", location="westus", sku="standard" ) # Configure a new cluster or refer to an existing one that the Databricks Job will use. # Here, we specify the node type and the number of workers for the cluster. cluster = azure.databricks.Cluster("ml-cluster", cluster_name="ml-cluster", spark_version="5.3.x-scala2.11", node_type_id="Standard_D3_v2", spark_conf={"spark.speculation": True}, num_workers=2 ) # Define a Databricks Job to run a machine learning notebook. # The job configuration includes the location of the notebook and the cluster to execute it. job = azure.databricks.Job("ml-job", existing_cluster_id=cluster.id, notebook_path="/Workspace/Notebooks/ml-notebook", max_retries=1, # You can define a cron schedule if you want the job to run at specific times. # For example, '0 */2 * * *' would run the job every 2 hours. schedule=azure.databricks.JobScheduleArgs( quartz_cron_expression="0 0 * * * ?", # This would run the job once every hour. timezone_id="America/Los_Angeles" ) ) # To run the job immediately or on triggers other than time, you'd configure the trigger settings accordingly. # Export the URL of the Databricks Workspace to access it from the Azure portal. pulumi.export('Databricks Workspace URL', workspace.workspace_url)

    Steps to run the Pulumi program:

    • Place the above code into a file named __main__.py.
    • Open your terminal and navigate to the directory containing your __main__.py file.
    • To preview the deployment without actually performing it, run pulumi preview.
    • To deploy the resources to your Azure account, run pulumi up.
    • You can access the Databricks Workspace using the URL exported at the end of the program. From there, you can manage and monitor your machine learning workflows.

    This program sets up a basic Databricks workspace and job to demonstrate the mechanism you can use to automate machine learning workflows. You may need to customize the cluster and job configurations based on your specific machine learning tasks, data sizes, and compute requirements.