Amazon SageMaker Feature Store
Fully managed repository to create, store, share, and serve ML features
Open-Source Projects
Discover top open-source software, updated regularly with real-world adoption signals.
Discover top open-source software, updated regularly with real-world adoption signals.
Unified feature store for training and real‑time inference
Feast delivers a consistent, low‑latency feature store that unifies offline batch processing and online serving, prevents data leakage, and decouples machine‑learning pipelines from data infrastructure.
Feast is a feature store that lets ML platform teams make features reliably available for both model training and real‑time inference. By managing an offline store for batch scoring and an online store for low‑latency lookups, it ensures that the same feature definitions are used throughout the ML lifecycle.
Feast generates point‑in‑time correct training datasets, eliminating data‑leakage bugs, and abstracts storage behind a single access layer so models stay portable across data warehouses such as Snowflake, Redshift, BigQuery, and others. Users install the Python SDK, define a feature repository, apply definitions, and materialize data via simple CLI commands (feast materialize, feast materialize-incremental). An experimental web UI (feast ui) provides visual exploration. The architecture supports a wide range of offline and online stores, making it adaptable to cloud, on‑prem, or hybrid environments.
When teams consider Feast, these hosted platforms usually appear on the same shortlist.
Fully managed repository to create, store, share, and serve ML features
Feature registry with governance, lineage, and MLflow integration
Central hub to manage, govern, and serve ML features across batch, streaming, and real time
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Batch model training with historical features
Generate point‑in‑time training datasets that avoid data leakage.
Real‑time inference serving
Retrieve low‑latency online features for live predictions.
Feature engineering experimentation
Iteratively register, materialize, and test new features without pipeline disruption.
Cross‑cloud data integration
Unify features from multiple warehouses (Snowflake, Redshift, BigQuery) under a single store.
Feast provides a Python SDK; additional language clients are available through community contributions.
Use `feast materialize` for full runs or `feast materialize-incremental` for incremental updates; the `--disable-event-timestamp` flag can simplify materialization when timestamps are missing.
Yes, Feast includes plugins for Snowflake, Redshift, BigQuery, Azure Synapse, Hive, Postgres, and more.
Feast offers an experimental web UI launched via `feast ui` for visual exploration of feature repositories.
Feast is widely adopted in production environments, though some components (e.g., NLP feature server) remain in alpha.
Project at a glance
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