1. Managed Database Endpoints for ML Model Serving


    To set up managed database endpoints for machine learning (ML) model serving, you usually need a system that can deploy your trained ML models, manage traffic for inference requests, and optionally, scale based on demand. Many cloud providers have services that can host and serve ML models, which involve creating an endpoint that client applications can use to make predictions using the hosted models.

    Pulumi allows you to define and deploy such ML-serving infrastructure as code, which promotes best practices like version control, repeatability, and automated deployments.

    In this program, I'll illustrate how to set up a managed endpoint for ML model serving using AWS SageMaker, a fully-managed service that allows you to build, train, and deploy machine learning models. SageMaker supports a variety of ML frameworks, including TensorFlow and PyTorch.

    Here's what we'll do:

    1. Define an Amazon SageMaker model.
    2. Define a SageMaker endpoint configuration, which specifies the hardware and networking setup for the endpoint.
    3. Create the endpoint, which serves predictions over a HTTPS API.

    SageMaker Model

    The aws.sagemaker.Model resource represents a SageMaker model. In your use case, "model" refers to the trained machine learning model artifacts. A SageMaker model includes information such as the location of the trained model artifacts in S3 and the Docker container image that contains inference code.

    SageMaker Endpoint Configuration

    Before you create an endpoint, you define its configuration using the aws.sagemaker.EndpointConfiguration resource. This configuration includes a variety of options, like what types of instances to use, scaling configuration, and production variants.

    SageMaker Endpoint

    Finally, the aws.sagemaker.Endpoint resource represents the actual live endpoint. This resource links to the endpoint configuration and effectively makes the model callable over a secured API endpoint.

    Here's a simplified Pulumi program to create a managed endpoint for ML model serving:

    import pulumi import pulumi_aws as aws # Create an AWS SageMaker model by specifying the Docker image containing the inference code, # and the S3 location of the trained model data. model = aws.sagemaker.Model("ml-model", execution_role_arn="arn:aws:iam::123456789012:role/service-role/AmazonSageMaker-ExecutionRole-20200101T000001", # replace with appropriate role primary_container={ "image": "123456789012.dkr.ecr.us-west-2.amazonaws.com/your-inference-image:latest", # specify your Docker image "model_data_url": "s3://your-s3-bucket/your-model-path/model.tar.gz" # specify the S3 URL to your model }) # Create a SageMaker endpoint configuration with resource specifications. # The instance type and model name are specified here. endpoint_config = aws.sagemaker.EndpointConfiguration("ml-model-config", production_variants=[{ "variant_name": "AllTraffic", # name of the production variant "model_name": model.name, # link to the model we created earlier "initial_instance_count": 1, # Minimum number of instances "instance_type": "ml.m4.xlarge" # Specify the ML instance type }]) # Create a SageMaker endpoint using the endpoint configuration. # This endpoint receives live traffic and can be used for predictions. endpoint = aws.sagemaker.Endpoint("ml-model-endpoint", endpoint_config_name=endpoint_config.name) # Export the endpoint name and URL to access it from client applications. pulumi.export("endpoint_name", endpoint.name) pulumi.export("endpoint_url", pulumi.Output.concat("https://runtime.sagemaker.us-west-2.amazonaws.com/endpoints/", pulumi.Output.get_string(endpoint.endpoint_name))) # To call the endpoint, you'd generally setup a secure API using something like AWS API Gateway or invoke it directly from your application using the AWS SDK.

    The Pulumi program sets up an endpoint that you can use to serve predictions from trained machine learning models. Here's how the components work:

    • The model represents your trained ML model. You'd put your model artifacts in an S3 bucket and specify the image that can run your model for inference.

    • The endpoint_config specifies how the endpoint should be provisioned. Here we've defined a single production_variant, which dictates how traffic is handled and what compute resources are used.

    • Finally, the endpoint creates a live HTTPS accessible endpoint for your ML model. You can secure this endpoint with AWS Identity and Access Management (IAM) roles and policies to control who can invoke the endpoint.

    After you run this Pulumi program using pulumi up, you can call the resulting endpoint_url with the input data to get predictions from your ML model.

    Keep in mind that this is a basic example. In a production setup, you may want to configure other features such as data capture, VPC configurations, autoscaling, and more. Pulumi and AWS SageMaker provide the flexibility to configure these according to your needs. For more information on AWS SageMaker with Pulumi, you can refer to the AWS SageMaker documentation in Pulumi.