Unified network architecture for global ML inference services.

Question

Pulumi · Accepted Answer

To implement a unified network architecture for global machine learning (ML) inference services using Pulumi and Azure, you will need to set up infrastructure that can handle the deployment and management of machine learning models, serving predictions at scale with low-latency global access. This often involves using services such as Azure Machine Learning, Azure Kubernetes Service (AKS) for container orchestration, and Azure Traffic Manager or Azure Front Door for global routing and load balancing.

In this Pulumi program, you'll see how to:

1. Create an Azure Machine Learning Workspace, which is a foundational piece for managing the lifecycle of your ML models.
2. Define an Inference Cluster where your trained ML models will be deployed.
3. Set up an Inference Endpoint, which allows interfacing with your deployed models seamlessly.
4. Implement global routing with Azure Traffic Manager or Azure Front Door to intelligently distribute traffic to the closest available inference endpoint for a given user.

Here's a Pulumi program written in Python that sets up each part of the network infrastructure:

```python
import pulumi
import pulumi_azure_native as azure_native

# Create Azure Resource Group
resource_group = azure_native.resources.ResourceGroup("resource_group")

# Create Azure Machine Learning Workspace
ml_workspace = azure_native.machinelearningservices.Workspace(
    "ml_workspace",
    resource_group_name=resource_group.name,
    location=resource_group.location,
    sku=azure_native.machinelearningservices.SkuArgs(
        name="Standard",
    ),
    description="ML Workspace for global inference"
)

# Define an Inference Cluster for deploying models
# An Inference Cluster is a managed, scalable set of compute resources for serving your model.
inference_cluster = azure_native.machinelearningservices.InferenceCluster(
    "inference_cluster",
    resource_group_name=resource_group.name,
    location=ml_workspace.location,
    workspace_name=ml_workspace.name,
    properties=azure_native.machinelearningservices.InferenceClusterPropertiesArgs(
        description="Cluster for serving ML models globally"
        # Further parameters can define specific requirements for the cluster
    )
)

# Set up an Inference Endpoint
# The endpoint is what allows you to interface with your deployed models.
# It needs to be associated with the Inference Cluster.
inference_endpoint = azure_native.machinelearningservices.InferenceEndpoint(
    "inference_endpoint",
    resource_group_name=resource_group.name,
    location=ml_workspace.location,
    workspace_name=ml_workspace.name,
    endpoint_name="global-ml-endpoint",
    properties=azure_native.machinelearningservices.InferenceEndpointPropertiesArgs(
        description="Endpoint for ML model serving"
        # Additional properties can specify authentication, compute targets, etc.
    )
)

# Implement global routing using Azure Traffic Manager to route users to the closest available endpoint
traffic_manager_profile = azure_native.trafficmanager.Profile(
    "traffic_manager_profile",
    resource_group_name=resource_group.name,
    traffic_routing_method=azure_native.trafficmanager.TrafficRoutingMethod.GEOGRAPHIC,
    profile_status=azure_native.trafficmanager.ProfileStatus.ENABLED,
    traffic_view_configuration=azure_native.trafficmanager.TrafficViewConfigurationArgs(
        enabled=True
    ),
    endpoints=[
        # Here you would list the endpoints in different geographical locations.
        # This example is simplified and assumes a single endpoint.
        azure_native.trafficmanager.EndpointArgs(
            name="endpoint1",
            endpoint_location=ml_workspace.location,
            target_resource_id=inference_endpoint.id,
            type="Microsoft.Network/trafficManagerProfiles/externalEndpoints"
        )
    ]
)

# Export the main traffic manager endpoint so it can be accessed globally
pulumi.export("traffic_manager_endpoint", traffic_manager_profile.fqdn)
```

### Explanation

The Pulumi program provided above sets up the following components:

- **Resource Group**: A container that holds related resources for an Azure solution.
- **Machine Learning Workspace**: An Azure resource that provides a central place for all ML activities performed on Azure. It keeps your resources organized and allows for orchestration of training and prediction workflows.
- **Inference Cluster**: A dedicated space for deploying your machine learning models with the necessary compute capacity. It’s essentially a Kubernetes cluster optimized for running machine learning workloads.
- **Inference Endpoint**: A managed endpoint that exposes your machine learning models for client applications to consume predictions.
- **Traffic Manager Profile**: A DNS-based traffic load balancer that distributes traffic optimally to services across global Azure regions, while providing high availability and responsiveness.

### How to Use

1. Modify the program to include specifics about your ML models and any additional requirements they may have for compute capacity, memory, etc.
2. Deploying this program will set up the infrastructure. After provisioning the infrastructure, you'll need to deploy your ML models to the Inference Cluster. This is typically done by registering the model with the Azure Machine Learning service and creating an endpoint for it within the workspace.
3. Configure the Traffic Manager with endpoints in the desired regions to provide a global entry point for the model’s consumers.

By running this Pulumi program, you are automating the setup of a scalable, globally-distributed machine learning inference architecture, overcoming the complexities of managing such infrastructure manually.