Automated Access Control for Azure Machine Learning

Question

Pulumi · Accepted Answer

Automated Access Control in the context of Azure Machine Learning involves managing permissions and access to Azure Machine Learning workspaces, datasets, models, and other resources. To create an automated access control setup using Pulumi, one might leverage Azure Active Directory and role assignments to control access to the Azure Machine Learning workspace.

I will demonstrate how to use Pulumi to create an Azure Machine Learning Workspace and configure automated access control by granting a role-based access control (RBAC) assignment to an Azure AD principal (user or group).

Here's how the process works:

1. **Create an Azure Machine Learning Workspace**: This is the foundational resource where all machine learning assets will be stored and managed.

2. **Configure Azure AD Principal**: You define which Azure AD user or group needs access to the workspace.

3. **Assign a Role to the Principal**: You create a role assignment that grants the Azure AD principal certain permissions over the workspace (e.g., `Contributor`, `Reader`, or a custom role).

Please keep in mind that for running this program, you must have the appropriate permissions to create resources and assign roles within your Azure subscription, and your Pulumi account should be configured with the relevant Azure credentials.

Let’s dive into the code.

```python
import pulumi
import pulumi_azure_native.authorization as authorization
import pulumi_azure_native.machinelearningservices as ml
import pulumi_azure_native.resources as resources

# You must replace these variables with the actual IDs of your Azure AD Tenant, Subscription, User or Group.
# To fetch these values, you can use Azure CLI, PowerShell, or Azure portal.
tenant_id = "your-azure-ad-tenant-id"
subscription_id = "your-azure-subscription-id"
principal_id = "your-azure-ad-principal-id"  # User or group

# Create a resource group to contain the Machine Learning Workspace
resource_group = resources.ResourceGroup("ml-resource-group")

# Create an Azure Machine Learning Workspace
ml_workspace = ml.Workspace("ml-workspace",
    resource_group_name=resource_group.name,
    sku="Basic",  # Choose the appropriate pricing tier
    location=resource_group.location,
)

# Create the role assignment for the specified principal (user/group)
role_assignment = authorization.RoleAssignment("role-assignment",
    principal_id=principal_id,
    principal_type=authorization.PrincipalType.SERVICE_PRINCIPAL,
    scope=ml_workspace.id,
    role_definition_id=f"/subscriptions/{subscription_id}/providers/Microsoft.Authorization/roleDefinitions/4d97b98b-1d4f-4787-a291-c67834d212e7",  # This is the ID for the "Contributor" role
)

pulumi.export("workspace_name", ml_workspace.name)
pulumi.export("workspace_url", ml_workspace.workspace_url)
pulumi.export("role_assignment_id", role_assignment.id)
```

In this program, we first create a new resource group named `ml-resource-group` that will contain our Azure Machine Learning Workspace and any accompanying resources. We then create a `Workspace` resource within this group with a basic SKU. Make sure to replace the `tenant_id`, `subscription_id`, and `principal_id` with appropriate values for your Azure subscription and the user/group you want to grant access to.

The `authorization.RoleAssignment` resource is used to grant the `Contributor` role to the principal we specified. This role allows the user/group to manage resources within the workspace, but not to access data or manage the access of others. You can customize this by selecting a different role definition ID that fits your access control requirements.

Finally, we export some of the resource IDs and URLs, which could be useful for accessing these resources programmatically or through the Azure portal.

To use this code, you'll need to install the `pulumi` CLI and set up the Azure provider. You can find instructions on how to do that in the [Pulumi Azure documentation](https://www.pulumi.com/docs/get-started/azure/).

Please be aware that granting permissions using RBAC can impact the security of your Azure resources, so make sure to grant only the necessary permissions to each principal.