1. Low-latency Serving of AI Model Predictions


    To serve AI model predictions with low latency, we'll use a cloud provider's service that is optimized for machine learning inference. In this case, we'll use the Azure Machine Learning service to create an inference endpoint. An inference endpoint is a web service that enables clients to get predictions from a deployed machine learning model with minimal latency.

    Here is a high-level flow of the steps we will follow in our Pulumi program:

    1. We'll create an inference endpoint using Azure Machine Learning Service's InferenceEndpoint resource.
    2. We'll configure the endpoint with the necessary details, including the machine learning model we want to deploy.
    3. We'll ensure that the endpoint is configured to provide the low-latency responses required for serving predictions.
    4. At the end of the Pulumi program, we'll export the URL of the inference endpoint so that it can be used by clients to obtain predictions.

    Let's proceed to write the Pulumi program that accomplishes these steps:

    import pulumi import pulumi_azure_native as azure_native from pulumi_azure_native.machinelearningservices import InferenceEndpoint # Set up an Azure Resource Group resource_group = azure_native.resources.ResourceGroup("ai_model_predictions_rg") # Create an Azure Machine Learning Workspace # Replace the `location` with your desired Azure region and set other required properties ml_workspace = azure_native.machinelearningservices.Workspace( "ml_workspace", resource_group_name=resource_group.name, location="East US", sku=azure_native.machinelearningservices.SkuArgs( name="Standard_DS3_V2", ), properties=azure_native.machinelearningservices.WorkspacePropertiesArgs( description="AML Workspace for low-latency AI model serving", ) ) # Deploy an Inference Endpoint # Replace `model_id` with the actual ID of the trained machine learning model you want to deploy inference_endpoint = InferenceEndpoint( "inference_endpoint", resource_group_name=resource_group.name, workspace_name=ml_workspace.name, location=ml_workspace.location, properties=azure_native.machinelearningservices.EndpointPropsArgs( # Set the compute type and other configurations needed for low latency compute_type="Dedicated", # Assign other properties as needed description="Low-latency Inference Endpoint for AI Model Predictions", # Include details specific to the model you are deploying model_ids=["<model_id>"], # Replace '<model_id>' with your actual model ID ), ) # Export the URL of the Inference Endpoint pulumi.export("inference_endpoint_url", inference_endpoint.properties.apply(lambda props: props.scoring_uri))

    In the program above:

    • We've created an Azure Resource Group to organize all related resources.
    • We've defined an Azure Machine Learning Workspace, which is required to host the inference endpoint.
    • We deployed the InferenceEndpoint, configuring it to use dedicated compute for low-latency predictions. Replace model_id with the ID of the trained model that will be serving predictions.
    • Finally, we exported the scoring URI of the endpoint, which clients will use to send data for predictions.

    Remember to replace placeholders like <model_id> with actual values from your Azure Machine Learning environment.

    You can run the Pulumi program using the Pulumi CLI. Ensure you've already authenticated with Azure and set up the Pulumi CLI. Deploy your stack by navigating to the program's directory and running pulumi up. This will provision all defined cloud resources and create your inference endpoint for low-latency AI model predictions.