I think for small teams with a small number of models, a feature store is probably overkill. Just like you wouldn't need a data warehouse if you only have one d

I think for small teams with a small number of models, a feature store is probably overkill. Just like you wouldn't need a data warehouse if you only have one database. However, with lots of sources of features (oltp database(s), data lake, kafka), it becomes very hard for data scientists to find/use the data they need to train models. The feature store acts like a data warehouse for features for data scientists - and the more features are reused within the organization the more value you will derive from the feature store. You wouldn't ask a Tableau user to go to S3 or Athena or Big Query to get their data for reporting, and at organizations with feature stores, their data scientists can find most of the features they need in the feature store (Uber have >20k features last i heard in their feature store). Then, there are problems related to making those features available to online applications and not duplicating feature engineering pipelines and monitoring of models that are covered elsewhere on this page and in the literature (http://www.featurstore.org http://www.featurstore.org).