An AI Engineer Technical Guide to Feature Store with FEAST
Dive into a comprehensive technical guide on Feature Store implementation using FEAST (Feature Store). Learn about the key data challenges in productionizing ML systems and how feature stores address them. Explore FEAST's capabilities as an open-source feature store, enabling on-demand transformations and combining request data with precomputed features. Follow along with a hands-on Jupyter Notebook demonstration covering FEAST installation, source data understanding, registry catalog and online store setup, and architecture review. Discover how to transform feature values, work with online and offline stores, and validate features. Gain insights into machine learning with online features, model saving, and scoring using historical data. Conclude with a content review, GitHub repository exploration, and future plans for using PostgreSQL as an online store.