Summary and Quiz
Explore how to orchestrate machine learning workflows with SageMaker Pipelines, track experiment metrics and model lineage, and apply hub-and-spoke governance strategies to manage enterprise-scale MLOps. Understand CI/CD integration, artifact management, and automation with event-driven triggers, preparing you to operationalize ML efficiently at scale.
Summary
This chapter covers how to orchestrate ML workflows with SageMaker Pipelines, track experiments and model lineage, and implement hub-and-spoke governance for enterprise-scale MLOps.
SageMaker Pipelines and CI/CD orchestration
SageMaker Pipelines models ML workflows as directed acyclic graphs, in which ProcessingStep, TrainingStep, EvaluationStep, ConditionStep, and RegisterModel map to ...