Feast is an open-source feature store for machine learning that offers organizations a robust solution for consistent storage and serving of features crucial for both offline training and online inference. It facilitates the management of an offline store for processing historical data at scale, a low-latency online store for real-time predictions, and a battle-tested feature server for serving pre-computed features online.
Feast empowers ML platform teams by ensuring features are consistently available for training and serving, preventing data leakage through correct point-in-time feature sets, and decoupling ML from data infrastructure. With Feast, data scientists can focus on feature engineering, and models remain portable across various stages and systems, from training to serving and from batch to real-time models.
