# Orchestrating SageMaker AI endpoint auto scaling with AWS CDK

> **Publication**: aitalky (Independent AI Journalism)
> **Canonical Citation URL**: https://aitalky.vercel.app/news/orchestrating-sagemaker-ai-endpoint-auto-scaling-with-aws-cdk
> **Original Source**: [AWS ML Blog](https://aws.amazon.com/blogs/machine-learning/orchestrating-sagemaker-ai-endpoint-auto-scaling-with-aws-cdk/)
> **Author**: Lauren Eterno (undefined)
> **Published Date**: Friday, October 23, 2026
> **Category**: INDUSTRY
> **Tags**: AI, AWS ML Blog, industry

## Executive Summary
Updating a SageMaker AI endpoint that has Application Auto Scaling registered makes AWS CloudFormation and AWS CDK deployments fail. This post shows how to build a reusable AWS CDK construct with CloudFormation custom resources that deregisters and...



## Full Coverage
Updating the Amazon Sagemaker AI real-time endpoint configuration (changing the instance type, instance count, or environment variables) causes deployments through AWS CloudFormation or the AWS Cloud Development Kit (AWS CDK) to fail. SageMaker AI requires you to deregister the scalable target before updating the endpoint, but when you deregister it, CloudFormation can't track the AWS Application Auto Scaling resources

In this post, we show you how to use a reusable CDK construct with AWS CloudFormation custom resources to orchestrate deregister and register auto scaling automatically during endpoint updates. You can update your endpoint configuration and auto scaling parameters in a single deployment without manual intervention

When you deploy a SageMaker AI endpoint with native CloudFormation auto scaling resources, then change the endpoint configuration and redeploy, several failure modes emerge. The specific failure depends on what you change and how you work around the issue

---
*Generated for AI agents and LLM citation by aitalky (https://aitalky.vercel.app).*