1. Granting Permissions for Training Pipelines on Azure ML Workspaces


    To grant permissions for training pipelines in an Azure ML workspace, we need to do the following:

    1. Create an Azure ML Workspace: This is the foundational block for machine learning activities. A workspace holds a collection of resources such as models, compute targets, data stores, and pipelines.

    2. Manage Workspace Access: We need to set up permissions for different users and roles to access the workspace resources.

    Let's go step by step through a Pulumi program in Python to accomplish this task.

    Step 1: Create an Azure ML Workspace

    We'll use the azure-native.machinelearningservices.Workspace resource type to create a workspace in Azure Machine Learning. The name, location, and resource group need to be provided. For additional security, we could specify an encryption configuration with a key vault.

    Step 2: Manage Workspace Access

    To manage the access control, we'll use the azure-native.machinelearningservices.WorkspaceConnection resource type. This allows us to define connections to the ML workspace for various types of interactions, such as accessing datasets or compute resources. You have detailed control over who can access, and what they can do within the workspace.

    Below is a Pulumi program that creates a new Azure ML workspace and then sets up a connection with required parameters:

    import pulumi import pulumi_azure_native.machinelearningservices as ml_services # Provide your resource group name and location resource_group_name = "my_resource_group" resource_group_location = "EastUS" # Create an Azure Machine Learning workspace ml_workspace = ml_services.Workspace( "my_ml_workspace", # The arguments are typically strongly typed; here, we're passing the resource group name and location resource_group_name=resource_group_name, location=resource_group_location, workspace_name="my_ml_workspace_name", sku=ml_services.SkuArgs( name="Basic" # Choose between Basic, Enterprise, etc., depending on your needs ), # Define identity type of the workspace, for example, "SystemAssigned" identity=ml_services.IdentityArgs( type="SystemAssigned" ), # Other optional properties can be set as per need like tags, description, # friendly_name, encryption settings, etc. ) # Manage access control to an Azure Machine Learning Workspace workspace_connection = ml_services.WorkspaceConnection( "my_workspace_connection", properties=ml_services.ConnectionPropsArgs( category="Workspace", target="workspaceResourceId", # Specify the actual resourceId where the access is to be granted value="<workspace_resource_id>" # Placeholder for actual resource ID ), resource_group_name=resource_group_name, workspace_name=ml_workspace.name, connection_name="my_connection_name" ) # Export the outputs pulumi.export("workspace_name", ml_workspace.name) pulumi.export("workspace_url", ml_workspace.workspace_url)

    This program creates a new Azure ML workspace with the specified properties, and then it sets up a workspace connection that can be used to grant access to the workspace's resources. The pulumi.export statements make it possible to retrieve the name of the workspace and its URL as outputs after the Pulumi program has been applied.

    When running the Pulumi program, ensure that you replace placeholders like <workspace_resource_id> with actual values that you intend to use in your Azure environment.

    Remember to have the Azure CLI installed and configured with the appropriate permissions to act on your Azure subscription. Pulumi will use the credentials from the Azure CLI to create and manage resources in Azure. Run pulumi up in the terminal from the directory containing the above script to apply the changes to your Azure cloud environment.