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Feature stores remove the need to build the operational AI support, reducing headcount and operational costs for small teams, enabling them to focus on the more valuable work of building the ML pipelines and AI-enabled applications or systems.

The Feature Store is where the features are stored and organized for the explicit purpose of being used to either train models (by Data Scientists) or make predictions (by applications that have a trained model).

What are the advantages of a feature store? Save time through advanced feature reusability. Creating features is a time-consuming process that requires domain expertise about the problem. ... Improve collaboration. ... Make model training and model inference consistent.

A feature store also provides other essential functionality such as data monitoring, version control, and access control.

A feature registry contains standardized feature definitions and associated metadata to act as a single source of information for an organization. The Feature Store makes searching through available Features and Feature definitions simple and straightforward.

The Feature Store is where the features are stored and organized for the explicit purpose of being used to either train models (by Data Scientists) or make predictions (by applications that have a trained model).

The Architecture. The physical feature store consists of a metadata layer, an inference store, a training store, and a transformation engine. Unlike a virtual feature store, the physical feature store comes with its own storage and transformation layer. It replaces existing data infrastructure.

As discussed earlier, feature stores contain data related to specific variables important for ML model training, while data warehouses store domain-specific data. However, the difference runs deeper in terms of structure. Data warehouses store data in relational databases with a well-defined schema.