Guide — Updated August 2026
How to turn ML models into reproducible, governed, monitored production systems on SageMaker — explained.
Models that work in notebooks but no repeatable path to production? This guide explains how SageMaker MLOps closes that gap.
SageMaker MLOps is how you make machine learning reproducible and reliable on AWS. This guide covers the building blocks: SageMaker Pipelines for orchestration, Managed MLflow for experiment tracking, Model Registry for versioning and approval, CI/CD with CodePipeline/CodeBuild/ECR and GitHub Actions, IaC with Terraform/CDK/CloudFormation, and inference and drift monitoring — plus how FMOps/LLMOps extends this for Bedrock and GenAI. It reflects the AWS state through August 2026 and links to hands-on support.
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