SageMaker Endpoints: Deploy Your Model to Production with Terraform 🚀

Training a model is half the battle. Deploying it to a scalable, auto-scaling endpoint with blue/green deployment and rollback is the other half. Here's how to deploy SageMaker real-time endpoints with Terraform. In the previous post , we set up the SageMaker Studio domain - the workspace where your team trains models. Now comes the production side: taking a trained model and deploying it to a scalable HTTPS endpoint that your applications can call for real-time predictions. SageMaker endpoints involve three Terraform resources: a Model (what to serve), an Endpoint Configuration (how to serve it), and an Endpoint (the live HTTPS URL). Add autoscaling and deployment policies on top, and you have a production-grade inference system. 🎯 🏗️ The Three-Layer Architecture Model (container image + model artifacts in S3) ↓ Endpoint Configuration (instance type, count, variants) ↓ Endpoint (HTTPS URL, auto-scaling, blue/green deployment) Resource What It Defines Model Container image + S3 model a