Model Registry with Versioning & Approval
Use SageMaker Model Registry to track model versions, manage approval workflows, and maintain a catalog of production-ready models. The foundation of MLOps governance.
AWS Services You'll Use
Lab Details
- Track
- MLA-C01
- Learning Path
- Pipeline Forge
- Difficulty
- Advanced
- Duration
- 30 min
- Environment
- Real AWS Sandbox
Why This Lab?
Unlike video courses or multiple-choice quizzes, this lab drops you into a real AWS sandbox where you build, deploy, and validate working infrastructure. Our automated validators check your actual AWS resources — not honor system, real proof. Complete it and it shows up in your verified portfolio with a timestamp and badge.
More from Pipeline Forge
Build a SageMaker ML Pipeline
Chain processing and training steps into an automated SageMaker Pipeline.
Advanced · 45 min🏗️Deploy Endpoints with CloudFormation
Provision SageMaker endpoints as infrastructure-as-code with CloudFormation.
Advanced · 35 min⚡Serverless ML Inference with Lambda
Deploy a scikit-learn model as a serverless Lambda API.
Advanced · 30 minReady to build this for real?
Get a safe AWS sandbox, step-by-step guidance, and automated validation. No risk of surprise charges. Your completed lab shows up in your verified portfolio.
Launch Cloud Edventures →