1. Scalable Backend for ML Model Inference Serving


    To create a scalable backend for ML model inference serving, we'll use Pulumi to define the necessary cloud infrastructure. In this context, "scalable" means the ability to handle varying levels of traffic without significant manual intervention. Ideally, the infrastructure should automatically scale resources up or down based on the current demand to ensure high availability and performance.

    One way to achieve this with Pulumi is by using the AWS SageMaker service. Amazon SageMaker provides fully-managed instances for deploying and running machine learning models, with a feature called endpoints, which allow you to create a scalable and secure API for your model, complete with automatic scaling based on the real-time demand.

    The following explains and demonstrates how to create a SageMaker model, endpoint configuration, and deploy an endpoint that serves inference requests using Pulumi and AWS.


    1. SageMaker Model: This resource represents the machine learning model you want to deploy. It requires a definition that includes the location of the trained model artifacts in S3 and the Docker container to be used for inference.

    2. SageMaker Endpoint Configuration: This configuration includes properties like the type and number of instances that serve the model's inferences. It also allows you to define how the scaling should behave.

    3. SageMaker Endpoint: This deploys the actual HTTP(S) endpoint tied to the model and endpoint configuration. It's this URL that applications will use to obtain inferences from the model.

    The example program below will guide you through setting up these resources using Pulumi and Python.

    import pulumi import pulumi_aws as aws # Placeholder for your trained model's S3 bucket and S3 key model_data_s3_bucket = 'your-model-s3-bucket' model_data_s3_key = 'your-model-data-s3-key' # The ARN of the IAM role SageMaker can assume to access the model artifacts and Docker image role_arn = 'arn:aws:iam::123456789012:role/service-role/AmazonSageMaker-ExecutionRole' # Define a SageMaker ML Model that points to your trained model's artifacts and the inference code container sagemaker_model = aws.sagemaker.Model("my-sagemaker-model", execution_role_arn=role_arn, primary_container={ "image": "123456789012.dkr.ecr.us-west-2.amazonaws.com/my-inference-container:latest", "model_data_url": f"s3://{model_data_s3_bucket}/{model_data_s3_key}", }) # Create a SageMaker Endpoint Configuration with an initial instance count and instance type endpoint_config = aws.sagemaker.EndpointConfiguration("my-endpoint-config", production_variants=[{ "instance_type": "ml.m5.large", "initial_instance_count": 1, "model_name": sagemaker_model.name, "variant_name": "AllTraffic", # Autoscaling policies could be attached here }]) # Deploy a SageMaker Endpoint using the configuration sagemaker_endpoint = aws.sagemaker.Endpoint("my-sagemaker-endpoint", endpoint_config_name=endpoint_config.name) # Export the endpoint name, which serves as the URL to send inference requests pulumi.export("endpoint_name", sagemaker_endpoint.endpoint_name)


    • The code begins by importing the necessary Pulumi and AWS SDK modules.
    • We then define some placeholders for the S3 bucket and key where your trained model artifacts reside. You would replace these with your actual S3 paths.
    • We've also defined an IAM role ARN that the SageMaker service will assume. Replace it with the ARN of an IAM role you've set up with permissions for SageMaker.
    • Next, we define the aws.sagemaker.Model resource, giving it a unique name and specifying the Docker image for the inference container as well as the S3 path to the model's data.
    • With the model in place, an aws.sagemaker.EndpointConfiguration is created, specifying the instance type and initial count for the deployment. This configuration also supports attaching autoscaling policies.
    • Finally, an aws.sagemaker.Endpoint is deployed. This creates a SageMaker endpoint with the specified configuration, effectively launching the service that your application will interact with.
    • The last line in the code exports the endpoint name, which is the identifier for the endpoint that will receive real-time inference requests.

    With this setup, the endpoint can automatically adjust to the traffic demands ensuring that you're using the most cost-effective resources based on load, while also providing low-latency responses.

    Keep in mind that this is a basic setup. Typically, you would also add autoscaling policies to the endpoint configuration depending on your application’s needs. Furthermore, make sure the IAM role has the necessary permissions and that your S3 bucket and keys are correctly specified.