Secure Ingress/Egress for Azure ML Workspaces

Question

Pulumi · Accepted Answer

When setting up secure ingress and egress for Azure Machine Learning (ML) Workspaces, there are several Azure services and resources that work in conjunction to protect your ML environment. The primary goal is to ensure that only authorized traffic can enter (ingress) or leave (egress) the ML workspace, which is critical for maintaining data privacy and regulatory compliance.

Here's what we'll do to ensure secure ingress and egress:

1. **Azure ML Workspace**: The foundation of our setup is the Azure ML Workspace, which acts as the central hub for your ML experiments, data, models, etc.

2. **Managed Network Settings Rule**: We will use managed network settings to control the traffic flow into and out of the ML workspace. We can specify IP ranges that are allowed to connect, enforce private link access, and more.

3. **Workspace Connection**: This will allow us to define connections to other Azure resources. For example, if the ML workspace needs to access data in an Azure Storage account, we'd use a workspace connection to set up a private endpoint.

4. **Private Link Scoped Resource**: Azure Private Link brings Azure services into your private network. It helps you to securely access Azure service resources from your virtual network via a private endpoint.

### Building the Pulumi Program

Below is a Pulumi program written in Python that sets up a secure Azure ML Workspace with the specified ingress and egress rules. The program will utilize `azure-native` Pulumi provider for Azure, which provides a native Pulumi experience with Azure resources. The resource classes used in the program correspond to the Azure services that will help us configure the secure environment.

Here's how the following Pulumi code achieves secure ingress and egress:

- It declares a new ML Workspace within a specified resource group and location.
- Sets up a `ManagedNetworkSettingsRule` to specify network settings like private endpoint connections and public network access to the workspace.
- Establishes a `WorkspaceConnection` to create necessary connections for the workspace to interact with other Azure resources securely.

```python
import pulumi
import pulumi_azure_native.machinelearningservices as azure_ml

# Replace these with the appropriate values for your environment
resource_group_name = "my-rg"
workspace_name = "my-ml-workspace"
location = "East US"

# Create an Azure ML Workspace
ml_workspace = azure_ml.Workspace(
    "mlWorkspace",
    resource_group_name=resource_group_name,
    workspace_name=workspace_name,
    location=location,
    # Additional properties can be set here if needed
)

# Configure managed network settings for ingress/egress rules
network_settings_rule = azure_ml.ManagedNetworkSettingsRule(
    "networkSettingsRule",
    rule_name="default",
    properties={
        # Replace with the settings you need; this is just an example
        "public_network_access": "Enabled",
        # Define other network settings like virtual network rules, private endpoint connections, etc.
    },
    workspace_name=workspace_name,
    resource_group_name=resource_group_name,
)

# Configure a workspace connection for secure access to other resources
workspace_connection = azure_ml.WorkspaceConnection(
    "workspaceConnection",
    connection_name="my-connection",
    properties={
        # Replace with your connection properties; this is just an example
        "target_resource_id": "/subscriptions/.../storageAccounts/myStorageAccount",
        "auth_type": "PAT",  # Personal Access Token, or other supported types
    },
    workspace_name=workspace_name,
    resource_group_name=resource_group_name,
)

# Export the ML Workspace URL for reference
pulumi.export("mlWorkspaceUrl", ml_workspace.web_url)
```

This Pulumi code will need to be executed by running the following commands in the terminal where Pulumi CLI has already been installed and configured with your Azure account:

```bash
pulumi up
```

This `pulumi up` will preview the changes and, upon your approval, apply them, setting up the secure Azure ML Workspace.

After execution, the program will output the URL to access your ML workspace, and you will have configured the network settings to ensure that the ingress and egress to the workspace are secured according to the specified rules. Make sure to customize the network settings and workspace connections according to your organization's specific security requirements and policies.