Real-Time Model Inference with Azure CDN Edge Computing

Question

Pulumi · Accepted Answer

To accomplish real-time model inference with Azure CDN (Content Delivery Network) edge computing, we need to deploy a machine learning model in a region close to your user base for low latency predictions. Azure Machine Learning service allows the deployment of models as web services that can be consumed from edge locations via Azure's global CDN.

Here's how you can do this using Pulumi to define and deploy the necessary infrastructure:

1. **Set up Azure Machine Learning Workspace:** This is a foundational service that brings together all the necessary Azure resources under a single workspace. It's where you'll manage models, compute resources, and deployments.

2. **Register the Model:** You'll register your pre-trained machine learning model with the Azure ML service. This usually means uploading the model file(s) into the workspace's cloud storage, from which it can be deployed.

3. **Create an Inference Cluster:** This is the compute resource that will run your model. It can be scaled according to your performance and cost requirements.

4. **Deploy the Model to an Endpoint:** Once the infrastructure is in place, you deploy your model as an Azure ML Inference Endpoint. This endpoint is what applications and services will interact with to get predictions.

5. **Integrate with Azure CDN:** To integrate the endpoint with Azure CDN for edge computing, you will use Azure Front Door or a similar service to route traffic through the CDN, bringing the inference close to the user.

Below is a Python program using Pulumi for deploying a machine learning model and hooking it into Azure's CDN for real-time inference at the edge:

```python
import pulumi
import pulumi_azure_native as azure_native

# Provide necessary configuration for Azure resources
resource_group_name = "my-resource-group"
workspace_name = "my-ml-workspace"
model_name = "my-ml-model"
inference_cluster_name = "my-inference-cluster"

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

# Create an Azure ML Workspace
ml_workspace = azure_native.machinelearningservices.Workspace("ml_workspace",
    resource_group_name=resource_group.name,
    workspace_name=workspace_name,
    location=resource_group.location,
)

# Register a machine learning model
# In a real-world scenario, this step would include uploading the actual model file, which might be a .pkl or similar.
model = azure_native.machinelearningservices.Model("ml_model",
    resource_group_name=resource_group.name,
    workspace_name=ml_workspace.name,
    model_name=model_name,
    # Additional properties like model path, version, etc. go here
)

# Create an Inference Cluster (AKS) to deploy the model
inference_cluster = azure_native.machinelearningservices.AksCompute("inference_cluster",
    resource_group_name=resource_group.name,
    workspace_name=ml_workspace.name,
    compute_name=inference_cluster_name,
    properties=azure_native.machinelearningservices.AksComputeArgs(
        agent_count=3,
        vm_size="Standard_D3_v2",
        # Additional configuration as needed
    ),
)

# Deploy the model to an online endpoint
endpoint = azure_native.machinelearningservices.Endpoint("endpoint",
    resource_group_name=resource_group.name,
    workspace_name=ml_workspace.name,
    properties=azure_native.machinelearningservices.EndpointPropsArgs(
        compute_type="AKS",
        deployment_target_id=inference_cluster.id,
        # Additional configuration like endpoint authentication, traffic rules, etc.
    ),
)

# Output the endpoint URL
pulumi.export("endpoint_url", endpoint.endpointUri)
```

This program sets up the necessary Azure infrastructure to serve a machine learning model for real-time inference. Note that specific steps like uploading the actual model file are not detailed here and would require additional code to handle the model artifacts. Once deployed, you would modify your Azure Front Door configuration to point to the Azure ML Online Endpoint as the backend, effectively integrating it with Azure CDN.

Remember that while Pulumi is used to define and deploy infrastructure, the machine learning development workflow—including training and evaluating the model—isn't covered by this program. You'd perform those steps using Azure ML's capabilities or other tools of your choice before registering the model to Azure ML via Pulumi.