SageMaker Studio Domain with Terraform: Your ML Workspace on AWS 🔬

SageMaker Studio is the IDE for ML on AWS - notebooks, training, deployment, all in one place. Here's how to provision the entire domain with Terraform including VPC, IAM, user profiles, and security hardening. In Series 1-3, we worked with managed AI services - Bedrock for models, Knowledge Bases for RAG, Agents for orchestration. Series 5 shifts to custom ML - training your own models, deploying them to endpoints, managing features, and building CI/CD pipelines. It all starts with a SageMaker Studio Domain. The domain is the foundation for everything in SageMaker - it provides the IDE (JupyterLab, Code Editor), manages user profiles, attaches shared storage (EFS), and controls network access. Think of it as the workspace where your ML team lives. This post provisions the entire setup with Terraform. 🎯 🏗️ What a SageMaker Domain Contains Component What It Does Domain Top-level resource: VPC config, auth mode, encryption User Profile Per-user config: execution role, instance types, app