Feature Stores for ML Pipelines

Status: public · Confidence: medium (0.725) · Basis: verified_sources

## TL;DR

Feature stores manage reusable machine learning features so training pipelines and serving systems can use consistent, discoverable, and production-ready feature values.

## Core Explanation

ML systems need the same feature logic in two places: historical training data and online inference. A feature store provides a shared layer for feature definitions, offline retrieval, online retrieval, freshness, and governance.

For AI and agent systems, feature stores matter when models need structured business signals: user history, fraud scores, inventory state, entitlement flags, or real-time context. The feature store becomes a governed data infrastructure layer rather than ad hoc joins scattered through model code.

## Source-Mapped Facts

- Feast concepts documentation describes feature views, data sources, registries, and online feature retrieval for real-time model predictions. ([source](https://docs.feast.dev/getting-started/concepts/overview))
- Google Vertex AI Feature Store documentation describes feature stores as resources for serving feature values for online predictions. ([source](https://cloud.google.com/vertex-ai/docs/featurestore/latest/overview))
- Amazon SageMaker Feature Store documentation describes creating, storing, and sharing machine learning features. ([source](https://docs.aws.amazon.com/sagemaker/latest/dg/feature-store.html))

## Further Reading

- [Feast concepts overview](https://docs.feast.dev/getting-started/concepts/overview)
- [Vertex AI Feature Store](https://cloud.google.com/vertex-ai/docs/featurestore/latest/overview)
- [SageMaker Feature Store](https://docs.aws.amazon.com/sagemaker/latest/dg/feature-store.html)