Question

An ML engineering team deploys SageMaker real-time endpoints across development, staging, and production accounts. Currently each environment is provisioned manually through the console, causing configuration drift and inconsistent instance types between accounts. The team wants a programmatic, version-controlled way to define the endpoint infrastructure using a familiar general-purpose programming language (TypeScript), reuse the same construct across all three accounts, and integrate the deployment into their existing CI/CD pipeline. Which approach best meets these requirements?

Accepted Answer

Use the AWS CDK to define the model, endpoint configuration, and endpoint as reusable constructs in TypeScript, synthesize CloudFormation templates, and deploy the same stack to each account through the CI/CD pipeline.

Answer

Write a Python script using the boto3 SDK that calls CreateModel, CreateEndpointConfig, and CreateEndpoint, and run it manually in each account when a deployment is needed.

Answer

Author a hand-written CloudFormation JSON template for each environment separately, editing instance types inline per account before uploading each template to the console.

Answer

Use SageMaker Pipelines steps to create the endpoints and rely on pipeline parameters to switch between accounts at runtime.