Glossary

Feature store

A centralized system that stores, versions and serves machine learning features so training and live prediction use identical definitions.

A feature store is a centralized system for storing, versioning and serving the features used to train and run machine learning models, built to solve a specific problem: the code that computes a feature for model training and the code that computes it for live predictions are often written separately and can drift apart, quietly breaking model accuracy.

A feature store typically has two sides: an offline store, holding historical feature values for training data, and an online store, serving the latest value for a given entity, such as a customer, with low latency at prediction time. Both are computed from the same underlying feature definition, so what a model saw during training matches what it sees in production, avoiding what practitioners call training-serving skew.

Feature stores matter because they let feature engineering work be defined once and reused across models and teams, instead of being reimplemented per project, and they make features easier to check for data drift over time. They are a core piece of MLOps tooling; teams without one often rebuild feature logic by hand for each model, increasing the risk of subtle mismatches between training and serving.

Last reviewed September 22, 2026

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Related terms

Feature engineering Creating, transforming or selecting the input variables a model is trained on, to make the underlying pattern easier for it to learn. MLOps (machine learning operations) The set of practices for deploying, monitoring and maintaining machine learning models reliably in production, extending DevOps to ML systems. Training data The dataset with known outcomes that a machine learning model learns patterns from before being used to predict new cases. Data pipeline The automated sequence of steps that moves data from source systems to a destination, transforming it along the way. Model monitoring Ongoing tracking of a deployed model's inputs, predictions and outcomes to catch accuracy loss, drift or failures after launch.

Related tools

Amazon SageMaker AWS's fully managed service for building, training, and deploying machine learning models at cloud scale. Databricks Feature Store Feature registry and serving layer built into Databricks Unity Catalog, using Delta tables as feature tables. Feast Open-source feature store that manages feature definitions, storage, and serving so ML models get consistent features in training and production. Featureform Virtual feature store that turns existing data infrastructure into a governed feature store; now developed by Redis as Redis Feature Form. Google Vertex AI Google Cloud's unified platform for AutoML, custom model training, and access to foundation models. Hopsworks Feature store and MLOps platform pairing a governed feature store with model registry and serving, available self-hosted or as SaaS.

Related guides

How to choose a feature store The real choice is whether your cloud or warehouse already has one built in, and whether you need sub-millisecond serving or just a shared registry.