1. Scalable Machine Learning Workflows with Databricks Service Principal Roles


    To set up a scalable machine learning workflow in Databricks using Service Principal Roles, you will need to create a number of resources. Firstly, you'll need the Databricks Service Principal which acts as a type of identity in Azure Databricks, allowing you to automate, simplify, and secure resource management. Additionally, assigning Service Principal Roles to the principal will allow you to define a set of permissions to access and execute actions within an Azure Databricks workspace.

    Here's what you need to set up:

    • Service Principal: This is the identity created for use with applications, hosted services, and automated tools to access Azure resources.
    • Service Principal Secret: The credential for authentication used by the service principal.
    • Service Principal Role: Defines the set of permissions for the service principal, ensuring it can only perform the actions needed for the machine learning workflows.
    • Databricks Cluster: The computing environment for running the workflows.
    • Model Serving: If you also want to deploy models as REST endpoints, you would also set up Model Serving.

    Below is a Pulumi Python program that outlines how to provision these necessary resources. This program does not run a specific machine learning workflow but sets the stage for you to deploy your models and workflows with the necessary permissions and infrastructure.

    import pulumi import pulumi_databricks as databricks # Before running this program, ensure that you have configured Pulumi for Databricks. # You should have a Databricks workspace set up and the necessary credentials and configurations for Pulumi. # Create a Databricks service principal. This acts like a user identity for your applications. service_principal = databricks.ServicePrincipal("my-service-principal", active=True, display_name="MyMLServicePrincipal", # application_id, acls, and other properties to match your setup ) # Create a secret scope for securely storing the secret associated with the service principal. # In your real-world scenario, make sure to handle secrets with care following best practices. secret_scope = databricks.SecretScope("my-secret-scope", initial_manage_principal="users", # Change this according to who should initially manage the scope. ) # Create a service principal secret. This is the credentials used by the service principal to authenticate. service_principal_secret = databricks.Secret("my-service-principal-secret", string_value="<YOUR_SECRET_HERE>", scope=secret_scope.name, key="service-principal-secret-key" ) # Assign the service principal a role which defines permissions within the Databricks workspace. # The role should be created to align with your security practices and the principle of least privilege. service_principal_role_assignment = databricks.ServicePrincipalRole("my-service-principal-role", service_principal_id=service_principal.id, # Define the role to assign to the service principal. # Options include roles like "Admin", "Contributor", and so on, depending on your workspace and requirements. role="<ROLE_HERE>" ) # Create a Databricks cluster for running machine learning jobs. # The configuration can be adjusted based on the workload requirements. cluster = databricks.Cluster("my-ml-cluster", # Define node types, scaling, and other properties to match the demands of your machine learning workload. num_workers=4, autoscale=databricks.ClusterAutoscaleArgs( min_workers=2, max_workers=8 ), spark_version="9.1.x-scala2.12", node_type_id="Standard_D3_v2", driver_node_type_id="Standard_D3_v2", # Additional properties such as instance pools, libraries, and security settings can be added based on requirements. ) # Optionally, if you are also serving models via REST endpoints, set up model serving. model_serving = databricks.ModelServing("my-model-serving", config=databricks.ModelServingConfigArgs( served_models=[ databricks.ModelServingConfigServedModelArgs( model_name="MyFirstModel", model_version="1", # Define environment, size, and other attributes as needed. ) ] # Define traffic configs if necessary. ), name="MyModelServingEndpoint", # Add tags or other attributes as desired. ) # Export the HTTP URL for the model serving endpoint if applicable. serving_url = pulumi.Output.all(service_principal, model_serving).apply( lambda args: f"https://{args[0].workspace_url}/{args[1].name}" ) pulumi.export("model_serving_url", serving_url)


    • This program assumes that you've already configured your Pulumi CLI with the Databricks provider and set up any required authentication.
    • The ServicePrincipal is being created with an active status and a display name.
    • A secret scope (SecretScope) is created where secrets will be stored.
    • The Secret resource defined here is where we would store the secret value that the service principal will use to authenticate; in practice, this should not be hardcoded, and you should use a secure method to manage this value.
    • ServicePrincipalRole assigns a defined role to the service principal, specifying the level of access control within Databricks.
    • A Databricks cluster (Cluster) is provisioned to execute machine learning jobs, with properties defined for number of workers, autoscaling, and node types.
    • As an optional step, ModelServing sets up a way to serve models through REST endpoints.

    Remember to replace placeholders like "<YOUR_SECRET_HERE>", "<ROLE_HERE>", and configure the Cluster and ModelServing with the correct properties suitable for your specific machine learning workloads.