Feast - A Feature Store for ML for GCP by Gojek/Google
An open-source feature store for managing and serving machine learning features for training and online inference.
What is Feast - A Feature Store for ML for GCP by Gojek/Google?
Feast is an open-source feature store designed to operationalize machine learning by managing the storage and serving of features. It provides a unified platform to make features consistently available for both model training and low-latency online inference, decoupling ML workflows from underlying data infrastructure. The project aims to be the fastest path to productionize analytic data for AI/ML, enabling teams to focus on feature engineering rather than infrastructure complexities.
ML platform teams and data engineers who need to manage and serve features for machine learning models in production, particularly those working across batch and real-time prediction environments. It is also suitable for data scientists seeking to avoid data leakage and ensure point-in-time correctness in feature datasets.
Developers choose Feast for its unified feature management that abstracts infrastructure, ensuring model portability across different data systems and deployment environments. Its extensible architecture supports a wide range of data sources, offline stores, online stores, and transformations, handling both batch and real-time data ingestion with push-based streaming and incremental materialization.
Overview
The Open Source Feature Store for AI/ML
Use Cases
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