SageMaker Model Registry, Lineage, and Governance for Beginners

Scaling machine learning from a single notebook to a production environment requires strict control over model versions, audit trails, and deployment approvals. Without proper governance, tracking how a model was trained and which dataset was used becomes nearly impossible. This text-only course guides you through establishing a robust machine learning operations (MLOps) foundation on AWS. You will learn how to catalog models, track their entire lifecycle from data to deployment, and document their behavior for compliance and auditing. What you'll learn: - Understand foundational model governance and registry concepts in MLOps. - Register, version, and manage machine learning models using SageMaker Model Registry. - Track data and model lineage to trace the exact history of your machine learning assets. - Create and manage SageMaker Model Cards to document model performance and intended use. - Implement approval workflows and access controls to secure your deployment pipeline. - Apply modern model auditing practices to ensure transparency and compliance. The course begins with the essential definitions of model governance, provenance, and the MLOps lifecycle. You will then read through step-by-step written guides and configuration examples to configure registries, track lineage, and generate model cards on AWS. This course is designed for aspiring MLOps engineers, data scientists, and cloud architects who want to implement model governance. No prior experience with SageMaker is required, though a basic understanding of machine learning concepts is helpful. Start reading today to bring structure, compliance, and reliability to your machine learning workflows.