Who benefits from a feature store?

Aside from the technical benefits of a feature store, one of the main benefits is organizational. In a typical enterprise ML team, the data engineers and data scientists have their own siloed workflows. As an over-simplification, data scientists will ask data engineers for new features and the engineers will provide them to the scientists. In this workflow, data scientists are decoupled from the data sources and are often unaware of problems providing and transforming data. Additionally, data engineers are decoupled from the usage of the data and are often unaware of problems implementing and utilizing data. 

In this context, a feature store will help both parties. It will allow both teams to gain a better understanding of the existing features, data sources, transformations, etc. It will also empower both parties to aid the other. For example, data scientists will have the ability to ingest from various data sources and perform transformations in batch and real-time. Additionally, data engineers will have the ability to see what features are currently available and retrieve them easily in batch and real-time. 

In this way, organizations can utilize a feature store to ease friction of hand-off points between teams. 

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