Secure Connection to Azure ML Workspaces via Service Endpoints.

Question

Pulumi · Accepted Answer

To establish a secure connection to Azure Machine Learning (Azure ML) Workspaces using service endpoints, you will need to create a virtual network with a service endpoint that provides secure and direct access to Azure ML Workspaces. In Azure, service endpoints extend your virtual network private address space and the identity of your virtual network to the Azure services, over a direct connection. This feature is mainly used for securing Azure service resources to only your virtual network.

Here's a Python program using Pulumi to set this up. This program will:

1. Create a resource group, which acts as a logical container for your Azure resources.
2. Deploy a virtual network and a subnet configured with the service endpoint for Azure Machine Learning.
3. Create an Azure ML Workspace with the necessary configurations to enable the connection from the service endpoint.

```python
import pulumi
from pulumi_azure_native import resources
from pulumi_azure_native import network
from pulumi_azure_native import machinelearningservices

# Create an Azure Resource Group
resource_group = resources.ResourceGroup('rg')

# Create an Azure Virtual Network
vnet = network.VirtualNetwork('vnet',
                              resource_group_name=resource_group.name,
                              address_space=network.AddressSpaceArgs(
                                  address_prefixes=['10.0.0.0/16']
                              ),
                              location=resource_group.location)

# Create a Subnet with Azure Machine Learning service endpoint
# The subnet is delegated to Azure Machine Learning using the service endpoint.
subnet = network.Subnet('subnet',
                        resource_group_name=resource_group.name,
                        virtual_network_name=vnet.name,
                        address_prefix='10.0.0.0/24',
                        service_endpoints=[network.ServiceEndpointPropertiesFormatArgs(
                            service='Microsoft.MachineLearningServices'
                        )],
                        delegations=[network.DelegationArgs(
                            name='mldelegation',
                            service_name='Microsoft.MachineLearningServices/workspaces'
                        )])

# Define the identity for the workspace
identity = machinelearningservices.IdentityArgs(
    type='SystemAssigned'
)

# Create an Azure ML Workspace
ml_workspace = machinelearningservices.Workspace('mlWorkspace',
                                                 identity=identity,
                                                 resource_group_name=resource_group.name,
                                                 location=resource_group.location,
                                                 sku=machinelearningservices.Sku(name="Standard"),
                                                 description='My ML Workspace')

# Export the necessary outputs
pulumi.export('resource_group_name', resource_group.name)
pulumi.export('virtual_network_name', vnet.name)
pulumi.export('subnet_id', subnet.id)
pulumi.export('ml_workspace_name', ml_workspace.name)
```

This Pulumi program uses the `pulumi_azure_native` package which provides native Azure Provider support within Pulumi.

- The `ResourceGroup` is a fundamental building block of this program that groups the Azure resources.
- The `VirtualNetwork` and `Subnet` are created to provide a network environment where you can create and manage Azure services securely.
- The `service_endpoints` argument within `Subnet` is configured specifically for Azure Machine Learning services.
- The `Workspace` represents the Azure Machine Learning workspace which will be connected securely through the subnet's service endpoint.

Ensure that Pulumi CLI and Python are installed, and you have authenticated with Azure and set the required configurations for Pulumi. Once that's all set, move the provided Python code into a file (e.g., `__main__.py`) and run `pulumi up` in the same directory to create the resources.

This setup will help you to ensure that data in your Azure ML Workspace can only be accessed from the configured virtual network, providing an additional layer of security.