Optimizing Machine Learning with SageMaker Pipelines

This chapter explores the advanced functionalities of Amazon SageMaker Pipelines, focusing on cost optimization techniques like step caching and selective execution for both novice and experienced users. It emphasizes the management of large language model projects, highlighting version control, experiment tracking, and the integration with tools like MLflow and GitHub to enhance model performance and streamline production transitions.