1. Serverless Endpoint Management for ML Microservices


    To manage serverless endpoints for machine learning (ML) microservices, you can use various cloud providers and their services. These endpoints usually serve the trained ML models to make predictions or inferences based on the input data they receive. Here, I will show you how to create and manage a serverless endpoint using Pulumi with Azure Machine Learning Services as an example.

    Azure Machine Learning provides two types of endpoints for serving models: Batch Endpoints, for batch inference scenarios, and Online Endpoints, for real-time inferencing. Depending on your use case, you can choose the endpoint type that suits your requirements best. For this demonstration, I'll focus on creating an Online Endpoint, which is suitable for real-time predictions with low latency.

    We will create an Azure ML Workspace, an Azure ML Online Endpoint, and a Deployment under that endpoint. Here's how to do it using Pulumi and the Python programming language:

    First, ensure you have the Pulumi CLI installed and configured with your Azure account.

    Now let's write a Pulumi program in Python:

    1. I'll import the required Pulumi Azure Native library to interact with Azure resources.
    2. We'll create an Azure ML Workspace which acts as a container for your ML assets.
    3. Then we'll define an OnlineEndpoint resource, specifying properties like the compute type required.
    4. Finally, we deploy an ML model to this endpoint, using the OnlineDeployment resource.

    Below is the code for the above setup:

    import pulumi import pulumi_azure_native as azure_native # Create an Azure Resource Group to organize related resources resource_group = azure_native.resources.ResourceGroup("resource_group") # Create an Azure ML Workspace ml_workspace = azure_native.machinelearningservices.Workspace( "ml_workspace", resource_group_name=resource_group.name, location=resource_group.location, sku=azure_native.machinelearningservices.SkuArgs( name="Basic", # Choose the SKU that fits your needs ), ) # Create an Azure ML Online Endpoint online_endpoint = azure_native.machinelearningservices.OnlineEndpoint( "online_endpoint", resource_group_name=resource_group.name, location=ml_workspace.location, online_endpoint_properties=azure_native.machinelearningservices.OnlineEndpointPropertiesArgs( auth_mode="Key", # Choose an authentication mode # Other properties can be set as required ), workspace_name=ml_workspace.name, # Setting a tag to identify the endpoint's lifecycle (optional) tags={"environment": "production"}, ) # Deploy an ML model to the online endpoint created above online_deployment = azure_native.machinelearningservices.OnlineDeployment( "online_deployment", name="initial-deployment", endpoint_name=online_endpoint.name, resource_group_name=resource_group.name, workspace_name=ml_workspace.name, online_deployment_properties=azure_native.machinelearningservices.OnlineDeploymentPropertiesArgs( endpoint_compute_type="Managed", # Specify the type of compute for this deployment # Additional properties to configure the model, such as model data source and resource requirements ), ) # Export the endpoint URL so we can call it later pulumi.export("endpoint_url", online_endpoint.scoring_uri)

    This program sets up a structure for an ML online endpoint in Azure using Pulumi. Here is what the code does:

    • ResourceGroup is a logical container into which Azure resources like our ML Workspace and Online Endpoint are deployed. These resources have to be part of a resource group.
    • Workspace is a foundational service in Azure Machine Learning which contains the machine learning training environment and endpoints.
    • OnlineEndpoint represents a service endpoint where the ML model will be deployed, capable of receiving data and returning predictions in real time.
    • OnlineDeployment is a specific deployment of a model into the endpoint. It can further be configured to auto-scale based on traffic, update with a new model, or rollback to the previous model version.
    • The endpoint_url is exported at the end of the program, which will be the URL you can send data to and receive predictions from the deployed ML model.

    By deploying this code using Pulumi, you automate the creation and management of these Azure resources. You can monitor, scale, and update your ML microservice endpoint as needed in a controlled and repeatable manner.