1. Subnet Configuration for Scalable AI Inference Endpoints


    In order to configure subnet(s) for scalable AI inference endpoints, we must create a virtual network (VNet) and define subnets within it. These subnets can then be associated with services that handle AI inference, making them accessible and scalable.

    The program below accomplishes this with Pulumi and Azure, by creating a VNet and a subnet. We then configure a Machine Learning Inference Endpoint within that subnet. The Inference Endpoint is part of Azure Machine Learning (AzureML) and can be used for deploying models and serving inference requests at scale.

    The AzureML Inference Endpoint, once provisioned, provides us with a fully managed service to serve real-time inference requests, and it's capable of auto-scaling based on the workload. Below the main program, I'll provide a brief description of the resources used.

    Here's the Pulumi program written in Python:

    import pulumi import pulumi_azure_native.network as network import pulumi_azure_native.machinelearningservices as ml # Define input values for the VNet and subnet. vnet_name = 'ai-inference-vnet' subnet_name = 'ai-inference-subnet' resource_group_name = 'my-resource-group' location = 'East US' # Replace with your preferred Azure region. # Create a resource group if not already existing. resource_group = network.ResourceGroup( "resource_group", resource_group_name=resource_group_name, location=location) # Create a virtual network. virtual_network = network.VirtualNetwork( "virtual_network", resource_group_name=resource_group.name, location=resource_group.location, address_space=network.AddressSpaceArgs(address_prefixes=[""]), virtual_network_name=vnet_name) # Create a subnet within the virtual network. subnet = network.Subnet( "subnet", resource_group_name=resource_group.name, address_prefix="", virtual_network_name=virtual_network.name, subnet_name=subnet_name) # Define an Azure Machine Learning Workspace (required for Inference Endpoint) workspace_name = 'my-ai-workspace' workspace = ml.Workspace( "workspace", resource_group_name=resource_group.name, location=resource_group.location, sku=ml.SkuArgs(name="Basic"), workspace_name=workspace_name) # Create an Azure Machine Learning Inference Endpoint. # Modify the 'kind' and other properties according to the specific needs. inference_endpoint = ml.InferenceEndpoint( "inference_endpoint", resource_group_name=resource_group.name, location=workspace.location, workspace_name=workspace.name, endpoint_name="my-ai-inference-endpoint", kind="Realtime", # Realtime or Batch depending on the use-case. tags={"purpose": "ai-inference"}, inference_endpoint_properties=ml.InferenceEndpointPropertiesArgs( # Configure specific properties for the Inference Endpoint. description="AI inference endpoint", groupId="inference-endpoint-group-id" ) ) # Export the Inference Endpoint URL. pulumi.export('endpoint_url', inference_endpoint.id)

    Explanation of resources used:

    • ResourceGroup: Represents a container that holds related resources for an Azure solution.
    • VirtualNetwork and Subnet: Network resources that provide isolation and organization of your cloud resources. AI inference endpoints will be attached to these subnets.
    • Workspace: An AzureML Workspace is a foundational resource in the cloud that you use to experiment, train, and deploy machine learning models.
    • InferenceEndpoint: An AzureML Inference Endpoint where deployed models can serve inference requests.

    The virtual network and subnet lay the foundational networking infrastructure required for creating isolated and secure environments for your AI workloads. The workspace is the logical grouping for all your AzureML resources, and the inference endpoint is where your trained models will be hosted and from where they can answer incoming prediction requests.

    Configure the inference_endpoint_properties according to the specifics of your AI application, such as authentication and computational requirements.

    This program should be adapted to accommodate specific requirements, such as the machine learning framework used, the size of the inference cluster, and potential integration with other Azure services.

    Once deployed, the endpoint_url can be used to send requests to the Machine Learning Inference Endpoint to obtain predictions from your trained model.