1. Assigning RBAC Roles for Azure ML Workspace Access


    In Azure, Role-Based Access Control (RBAC) is a strategy for restricting system access to authorized users. It's an essential aspect of security that ensures users have the access they need to Azure resources like Machine Learning workspaces, but nothing more. Pulumi allows you to express such access rules as part of your infrastructure code, granting and revoking permissions as part of your deployments.

    In Azure environments, RBAC roles and permissions are often assigned through a system of Role Assignments which bind roles to users, groups, or service principals on a particular scope, such as a resource, resource group, or subscription.

    Here's an example using Pulumi in Python for assigning a user to a specific role within an Azure Machine Learning workspace:

    • First, we will create an instance of an Azure Machine Learning workspace.
    • Then, we will assign a role to a user by creating a RoleAssignment resource.

    For the purpose of this example, we'll use a predefined role like "Contributor", but in a real scenario, you might want to use a more granular, custom role depending on your needs.

    Please ensure you have the Azure provider configured for your Pulumi program before running this code.

    Let's begin with the code:

    import pulumi import pulumi_azure_native as azure_native # Replace these variables with the details of the actual user and their role user_principal_id = "the-user-object-or-service-principal-id" role_definition_id = "the-role-id" # Role ID for the Contributor, Reader, etc. workspace_name = "my-ml-workspace" resource_group_name = "my-ml-resource-group" # Create an Azure Machine Learning workspace ml_workspace = azure_native.machinelearningservices.Workspace( "mlWorkspace", workspace_name=workspace_name, resource_group_name=resource_group_name, location="East US", sku=azure_native.machinelearningservices.SkuArgs( name="Basic" # Choose the appropriate SKU for your use case ), # Other necessary properties... ) # Get the ID of the scope where the role assignment will be applied, which is the ML workspace in this case workspace_scope = ml_workspace.id.apply(lambda id: id) # Create a role assignment to grant the specified user access to the Azure ML Workspace role_assignment = azure_native.authorization.RoleAssignment( "roleAssignment", scope=workspace_scope, role_definition_id=role_definition_id, principal_id=user_principal_id, # You could use a specific name for the role assignment or let Azure auto-generate one ) # To output the role assignment ID pulumi.export("role_assignment_id", role_assignment.id)

    In this example, we specify the role we wish to assign as well as the target user's principal_id and the role_definition_id. The scope is set to the ID of the workspace resource we've just created, meaning this role assignment applies to the workspace.

    We also export the role_assignment_id which is useful if you want to check or revoke the role assignment in the future.

    We have used a simple "Contributor" role in this example. You can, of course, create more specific roles and assign them similarly by defining your own role definitions.

    Please make sure that the principal_id and role_definition_id are correctly defined with the IDS of the user/service principal and the role you want to assign in your Azure Active Directory. The role definition id can be found in the Azure portal or via Azure CLI.

    Also, note that you may need to have sufficient permissions yourself to create resources or assign roles within the target scope.

    For further details on RBAC in Azure, visit the Azure RBAC documentation. For more information on managing Azure resources with Pulumi, you can review the Pulumi Azure Native provider documentation.