Secure Azure ML Studio Workspace with Private Endpoints

Question

Pulumi · Accepted Answer

To secure an Azure ML Studio workspace using private endpoints, you would typically set up an Azure Private Endpoint to ensure that the traffic between your Azure resources and Azure Machine Learning is confined to the Azure network, thus providing a more secure connection.

When you create the private endpoint, you also need to specify a private link service connection to the Azure Machine Learning workspace. The private link service allows your Virtual Network (VNet) to consume the Azure Machine Learning service privately. Azure resources like Virtual Networks and Private Endpoints are managed using the `azure-native` provider in Pulumi.

Here's how you secure an Azure ML Studio workspace using private endpoints with Pulumi:

1. **Create a Virtual Network (VNet)**: You will need a Virtual Network where the private endpoint will be located.

2. **Create a Subnet**: Create a dedicated subnet within the Virtual Network for the private endpoint.

3. **Create the Azure Machine Learning Workspace**: You need an Azure ML Workspace that you want to secure with the private endpoint.

4. **Initialize a Private Endpoint**: Set up the private endpoint in the subnet, and establish a connection to the Azure Machine Learning workspace.

5. **Set Up a Private Link Service Connection**: When creating the Private Endpoint, establish a private link service connection to the Azure Machine Learning workspace.

Here's a Pulumi program in Python to set up the resources described above:

```python
import pulumi
import pulumi_azure_native.network as network
import pulumi_azure_native.machinelearningservices as ml

# Configure variables or use existing ones, such as resource group name and location.
resource_group_name = "my-aml-resource-group"
location = "eastus"

# Create a new resource group if it doesn't exist
resource_group = network.ResourceGroup("resource_group", 
    resource_group_name=resource_group_name,
    location=location)

# Create the network and subnet for the private endpoint
vnet = network.VirtualNetwork("vnet",
    resource_group_name=resource_group.name,
    location=resource_group.location,
    address_space=network.AddressSpaceArgs(
        address_prefixes=["10.0.0.0/16"],
    ))

subnet = network.Subnet("subnet",
    resource_group_name=resource_group.name,
    virtual_network_name=vnet.name,
    address_prefix="10.0.1.0/24",
    # Setting the below to "Disabled" to allow private endpoints on this subnet
    private_endpoint_network_policies="Disabled")

# Create an Azure ML Workspace
ml_workspace = ml.Workspace("mlworkspace",
    resource_group_name=resource_group.name,
    location=resource_group.location,
    sku="Basic")

# Set up the private endpoint for the ML workspace
private_endpoint = network.PrivateEndpoint("privateEndpoint",
    resource_group_name=resource_group.name,
    location=resource_group.location,
    # Associate the private endpoint with the subnet
    subnet=network.SubnetArgs(id=subnet.id),
    # Define the connection to the ML workspace
    private_link_service_connections=[network.PrivateLinkServiceConnectionArgs(
        name="mlPrivateLinkServiceConnection",
        private_link_service_id=ml_workspace.id, # Link to the ML workspace
        group_ids=["amlworkspace"]  # Typically, the group ID for ML workspace connections
    )])

# Export the private endpoint's ID
pulumi.export("private_endpoint_id", private_endpoint.id)
```

This program does the following:

- Defines a `resource_group` for all of the resources.
- Creates a Virtual Network `vnet` with a single address space.
- Creates a `subnet` within that Virtual Network. Network policies for private endpoints must be disabled on the subnet.
- Provisions an Azure Machine Learning Workspace `ml_workspace`.
- Creates a `private_endpoint` within the subnet. The private endpoint is associated with the Azure ML Workspace.

When you apply this Pulumi program, these resources will be created and configured in your Azure subscription. This will ensure that the Azure ML workspace can only be accessed through the private endpoint in your virtual network, enhancing the security of your ML environment.

Ensure you have the correct permissions and that Azure Machine Learning supports private endpoints in your selected region and subscription before running this program.