Feast is an end-to-end open source feature store for machine learning. It allows teams to define, manage, discover, and serve features.

Feast's feature store is composed of two foundational components: (1) an offline store for historical feature extraction used in model training and an (2) online store for serving features …

Feast (Feature Store) is an open-source feature store designed to facilitate the management and serving of machine learning features in a way that supports both batch and real-time applications.

Jan 21, 2021 · A comprehensive guide to feature stores, their role in machine learning operations, and how they solve key challenges in the ML lifecycle.

Feast provides robust support for Generative AI applications, enabling teams to build, deploy, and manage feature infrastructure for Large Language Models (LLMs) and other Generative AI …

By implementing a feature store with Feast, teams can focus on model development rather than data engineering challenges, accelerating the delivery of ML applications to production.

Feast's architecture is designed to be flexible and scalable. It is composed of several components that work together to provide a feature store that can be used to serve features for training and …

On demand feature views allows data scientists to use existing features and request time data (features only available at request time) to transform and create new features.

Feast (Fea ture St ore) is a customizable operational data system that re-uses existing infrastructure to manage and serve machine learning features to realtime models.

Feast's feature store is composed of two foundational components: (1) an offline store for historical feature extraction used in model training and an (2) online store for serving features …