1. Low-Latency Machine Learning Model Deployment


    To accomplish a low-latency Machine Learning (ML) model deployment, you would typically need to set up a reliable and fast compute infrastructure that can run your ML inference code with minimal delay. I will guide you through deploying a machine learning model using Azure's services. The choice for Azure is motivated by a couple of Pulumi resources available for Azure Machine Learning services which are well-suited for ML model deployment.

    Firstly, Azure Machine Learning service provides a fully managed cloud service that enables you to easily build, deploy, and share predictive analytics solutions. We will use Azure Machine Learning's Online Endpoints to deploy the model as it provides a scalable and managed environment, which is ideal for achieving low-latency predictions. Online Endpoints enable you to deploy your models as RESTful services, making them accessible via HTTP requests.

    In this program, I will be using a couple of resources:

    • azure-native.machinelearningservices.Workspace: This resource allows us to create a workspace that serves as a container for all other Azure Machine Learning resources.
    • azure-native.machinelearningservices.Model: We register our ML model in the given workspace. The model can be any ML model that you've trained, and it's usually stored in a file like ONNX, PMML, TensorFlow, etc.
    • azure-native.machinelearningservices.OnlineEndpoint: Online endpoints are the resources through which we expose the model to the internet for real-time inference.
    • azure-native.machinelearningservices.OnlineDeployment: We deploy the model on the online endpoint. The deployment includes the configuration that determines how the model will handle predictions, including compute type and instance count.

    The code snippet below sets up an Azure ML workspace, registers an ML model, creates an online endpoint, and deploys the model onto the endpoint:

    import pulumi import pulumi_azure_native as azure_native # Create an Azure ML Workspace ml_workspace = azure_native.machinelearningservices.Workspace("myMlWorkspace", resource_group_name="my-resource-group", location="eastus2", sku="Basic") # Register an ML model within the created workspace ml_model = azure_native.machinelearningservices.Model("myModel", resource_group_name="my-resource-group", workspace_name=ml_workspace.name, model_name="my-lown-latency-model", # The properties below should be changed according to where the model is located # and the specifics of the ML model file. model=dict( model_url="https://my-model-storage/models/my-model.onnx", # Replace with the actual URL of your model description="A high performance ML model for low-latency predictions", frameworks=["Onnx"], # Update according to your model's framework # Additional model details go here )) # Create an Online Endpoint to expose the ML model as a service online_endpoint = azure_native.machinelearningservices.OnlineEndpoint("myOnlineEndpoint", resource_group_name="my-resource-group", workspace_name=ml_workspace.name, location="eastus2", endpoint_name="my-low-latency-endpoint", properties=dict( # Authentication, scale settings, and other endpoint configurations )) # Deploy the model on the Online Endpoint online_deployment = azure_native.machinelearningservices.OnlineDeployment("myOnlineDeployment", resource_group_name="my-resource-group", workspace_name=ml_workspace.name, endpoint_name=online_endpoint.name, deployment_name="my-deployment", # The deployment properties should contain information such as the instance type, the number of instances, and other # deployment-specific configuration details. It typically involves specifying the environment for your model: deployment_properties=dict( model_id=ml_model.id, # Link the registered model for deployment scaling=dict( min_instances=1, # Minimum number of instances for auto-scaling max_instances=5, # Maximum number of instances for auto-scaling ), # Additional deployment details go here )) # Export the endpoint URL so you can easily access it pulumi.export('endpoint_url', output=online_endpoint.properties.apply(lambda props: props["scoring_uri"]))

    Make sure to replace placeholder values like my-resource-group with the actual name of your Azure resource group, and update model_url with the actual URL where your ML model is stored.

    This program sets up the necessary resources for deploying an ML model in Azure. It aims for low-latency predictions by using Azure's managed services, which abstract away much of the underlying infrastructure complexity. After running this Pulumi program, you will have a running environment where you can start making real-time predictions through HTTP requests to the endpoint URL.