1. Deploying Multi-Region AI Model Endpoints in US-East-1


    To deploy multi-region AI model endpoints in the AWS US-East-1 region using Pulumi Python, you'll need to use the AWS SageMaker service. SageMaker is a fully managed service that provides the ability to build, train, and deploy machine learning (ML) models quickly.

    Below is a Pulumi Python program that deploys an AI model endpoint in the US-East-1 region. This example assumes you have a pre-existing SageMaker model and that you want to create an endpoint configuration and an endpoint for that model. For simplicity, we're not specifying multi-region configurations in this example because deploying actual AI models to multiple regions involves a series of steps including replicating model artifacts, setup in each target region, routing, etc., which is beyond the scope of a single Pulumi program.

    Here is the step-by-step explanation followed by the Pulumi Python program:

    1. Import necessary libraries: We start by importing the Pulumi library and the AWS package.
    2. Create a SageMaker model: This step would normally involve creating a new model, but in this example, we assume the model already exists.
    3. Create an Endpoint Configuration: The endpoint configuration specifies the resources required for an endpoint such as the types of instances and their number.
    4. Create an Endpoint: Finally, we create an AI model endpoint with the specified configuration.
    import pulumi import pulumi_aws as aws # Define existing SageMaker model name (replace with your actual model name) sagemaker_model_name = "my-pre-existing-sagemaker-model" # SageMaker Endpoint Configuration endpoint_config = aws.sagemaker.EndpointConfiguration("aiModelEndpointConfig", production_variants=[ aws.sagemaker.EndpointConfigurationProductionVariantArgs( instance_type="ml.m5.large", # Specify the instance type initial_instance_count=1, # Initial number of instances model_name=sagemaker_model_name, # Link to the existing model variant_name="AllTraffic", # Traffic distribution ) ], tags={ "Purpose": "multi-region-ai-endpoint", } ) # SageMaker Endpoint endpoint = aws.sagemaker.Endpoint("aiModelEndpoint", endpoint_config_name=endpoint_config.name, tags={ "Purpose": "multi-region-ai-endpoint", } ) # Export the endpoint name pulumi.export("endpoint_name", endpoint.name)

    Important Note: In this sample, we've focused on the deployment of a single-region endpoint for simplicity. Multi-region deployment would include additional steps such as setting up data replication, cross-region resource configuration, DNS routing (potentially with Route 53), and handling region-specific considerations like data residency and latency.

    The program exports the name of the deployed endpoint, which suggests that it is running and available for use. You can test the endpoint using the AWS SDK or CLI with the endpoint name provided.

    To run this Pulumi code:

    • Ensure you have Pulumi CLI installed and configured for AWS.
    • Install the required AWS SDK packages for Python.
    • Run pulumi up to deploy the changes.
    • Upon successful deployment, the endpoint name will be displayed as a stack output.