1. Role-based Access Control for Azure Machine Learning Workspaces

    Python

    When managing Azure Machine Learning Workspaces, having the appropriate access control is crucial. In Azure, Role-Based Access Control (RBAC) is a strategy that restricts system access to authorized users. Pulumi allows you to automate the provisioning of such resources and the application of RBAC rules.

    In the context of Azure Machine Learning, you can define who has what access to your Machine Learning Workspace by assigning roles to users, groups, and service principals. This process involves creating instances of the Workspace and assigning Role Assignments.

    Below, I will guide you through a Pulumi Python program that sets up an Azure Machine Learning Workspace along with RBAC.

    1. Azure Machine Learning Workspace: It is a foundational resource in the cloud that you use to experiment, train, and deploy machine learning models. We will create this workspace using Pulumi's azure-native.machinelearningservices.Workspace class.

    2. Role Assignments: Once the workspace is provisioned, we apply RBAC rules to it. Usually, this involves creating role assignments that link a role definition (the permissions) with a principal (the user or group granted those permissions). For this purpose, we will use azure-native.authorization.RoleAssignment resource.

    Let's go through the program step-by-step:

    import pulumi import pulumi_azure_native.authorization as authorization import pulumi_azure_native.machinelearningservices as machinelearningservices # Create an Azure Machine Learning Workspace. ml_workspace = machinelearningservices.Workspace("myMLWorkspace", resource_group_name=pulumi.Config('resource_group_name').require(), workspace_name="myUniqueWorkspaceName", sku=machinelearningservices.SkuArgs( name="Basic" ), location=pulumi.Config('location').require(), description="My ML Workspace for experiments", tags={ "environment": "development", } ) # Assign "Machine Learning Administrator" role to a specific user (principal) on the workspace. # The role definition ID for "Machine Learning Administrator" is known and fixed for Azure subscriptions as "bdf6c2f6-41e3-49e1-bb34-9c2f81a0fb59" role_assignment = authorization.RoleAssignment("machineLearningAdministratorRoleAssignment", scope=ml_workspace.id, role_definition_id="/subscriptions/{subscription_id}/providers/Microsoft.Authorization/roleDefinitions/bdf6c2f6-41e3-49e1-bb34-9c2f81a0fb59".format( subscription_id=pulumi.Config('azure:subscriptionId').require(), ), principal_id=pulumi.Config('principal_id').require() # This is the object ID of the user/service principal/group ) # Export the ID of the workspace and the role assignment ID. pulumi.export('workspace_id', ml_workspace.id) pulumi.export('role_assignment_id', role_assignment.id)

    Before you run this code, ensure you have the required configurations set on your environment:

    • resource_group_name: The name of the Azure resource group where you want to create the ML workspace.
    • location: The Azure region where you want to provision the workspace.
    • azure:subscriptionId: Your Azure subscription ID.
    • principal_id: The object ID of the user, service principal, or security group to which the Machine Learning administrator role should be assigned.

    You will run the program by invoking pulumi up on the command line, which will provision the resources as per the Pulumi project's settings. After the deployment, you will see the exported workspace_id and role_assignment_id in the Pulumi console, which are useful identifiers for your created resources.

    This program outlines a basic setup for RBAC on an Azure Machine Learning Workspace. Depending on your specific organization's access policies, you might need to create additional role assignments or use different roles.

    Learn more about Azure Machine Learning Workspace settings and configurations by visiting the official Pulumi documentation for the Machine Learning Workspace. For Role Assignments and defining custom roles, please check the Pulumi documentation about Azure Authorization.


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