1. Managed Websocket Endpoint for Real-time ML Predictions


    To create a managed WebSocket endpoint for real-time machine learning (ML) predictions, you'll want to use a cloud service that provides machine learning model hosting with real-time inference capabilities. Azure Machine Learning is a cloud service that allows you to build, train, and deploy machine learning models. One of its components, the Online Endpoint, lets you deploy a model as a web service, through which you can make real-time prediction requests.

    Here's a high-level overview of what we are going to do:

    1. Set up an Azure Machine Learning workspace, which is a foundational resource for machine learning on Azure.
    2. Create an Online Endpoint resource within the workspace. This endpoint will serve the prediction requests.
    3. Configure the endpoint with necessary details such as authentication mode and compute resources.

    Below is a Pulumi program written in Python that creates a managed WebSocket endpoint for real-time ML predictions using Azure Machine Learning:

    import pulumi import pulumi_azure_native as azure_native # Replace these variables with your specific details resource_group_name = 'my_ml_resource_group' workspace_name = 'my_ml_workspace' location = 'eastus' # Azure region where services will be deployed endpoint_name = 'my_realtime_ml_endpoint' # Set up an Azure Resource Group resource_group = azure_native.resources.ResourceGroup( resource_group_name, location=location ) # Create an Azure Machine Learning Workspace ml_workspace = azure_native.machinelearningservices.Workspace( workspace_name, location=location, resource_group_name=resource_group.name ) # Create an Online Endpoint for real-time ML predictions ml_online_endpoint = azure_native.machinelearningservices.OnlineEndpoint( endpoint_name, location=location, endpoint_name=endpoint_name, workspace_name=ml_workspace.name, resource_group_name=resource_group.name, online_endpoint_properties=azure_native.machinelearningservices.OnlineEndpointPropertiesArgs( # Configure authentication modes, compute resources, and more auth_mode="AMLToken" # This example uses Azure ML token auth mode ) ) # Export the endpoint URL pulumi.export('endpoint_url', ml_online_endpoint.endpoint_name.apply( lambda name: f"https://{location}.api.azureml.ms/{name}/{resource_group.name}/{workspace_name}/score" ))

    In this program:

    • We create a new resource group to contain our services using azure_native.resources.ResourceGroup.
    • We then set up an Azure Machine Learning workspace using azure_native.machinelearningservices.Workspace. A workspace is a working environment for managing and organizing machine learning resources within Azure.
    • We define an Online Endpoint using azure_native.machinelearningservices.OnlineEndpoint. This resource will act as the WebSocket endpoint to provide real-time ML predictions.

    The auth_mode in OnlineEndpointPropertiesArgs defines how authentication is handled for prediction requests. Here, "AMLToken" indicates that Azure Machine Learning tokens would be used for authentication.

    Finally, we output the endpoint URL where you can make WebSocket connections for sending real-time prediction requests to the ML model. This URL could be used in applications to perform real-time predictions.

    Remember, the actual deployment of a trained machine learning model to this endpoint and additional configuration for things like scaling and monitoring would also need to be done to operationalize the real-time prediction setup fully. The example above is a minimal configuration for creating the necessary cloud resources with Pulumi.