SageMaker Feature Store

Imagine you are building a recommendation system. In the absence of Feature Store, you’d navigate a landscape of manual feature engineering, scattered feature storage, and constant vigilance for consistency.

Feature management in an ML pipeline is challenging due to the dispersed nature of feature engineering, involving various teams and tools. Collaboration issues arise when different teams handle different aspects of feature storage, leading to inconsistencies and versioning problems. The dynamic nature of features evolving over time complicates change tracking and ensuring reproducibility. SageMaker Feature Store addresses these challenges by providing a centralized repository for features, enabling seamless sharing, versioning, and consistent access across the ML pipeline, thus simplifying collaboration, enhancing reproducibility, and promoting data consistency.

Now, user data, including age, location, browsing history, and item data such as...