Real time feature store

Feature Stores ingest raw incoming data, apply user-defined transformations to compute features from this data, store these features, and serve them to machine learning models.

Real time feature stores are typically designed to take into account two specific parameters of a feature: Frequency and Complexity. Frequency determines how a given feature is updated, which can be either real time or batch. Complexity describes the computational effort required to generate the feature.