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Method for centrally storing, versioning and serving ML features for training and inference.
A feature store is a centralized system for storing, versioning and serving ML features for training and inference. It unifies batch and real-time features, ensures consistency between training and production data, and improves reuse, governance and traceability in ML pipelines. It reduces engineering overhead and accelerates model delivery.
Measure of time until a feature is available for inference.
Share of models trained and served with identical feature versions.
Frequency of deviation between production and training distributions.
Feast is used as an example of a production-ready feature store pattern and demonstrates architecture and interface patterns.
Tecton illustrates a commercial managed feature store offering with governance and service levels.
Example of a hybrid implementation combining streaming and batch pipelines with a central serving layer.
Requirements analysis and definition of feature schemas
Select or build a feature store implementation
Implement batch and streaming pipelines
Introduce versioning, tests and CI/CD for features
Rollout, monitoring and training of user teams