A Feature Store is an essential piece of a production ML system. Twitter’s journey of building Feature Stores began several years ago. Since then, we have gone through multiple iterations of our Feature Store to facilitate creating, organizing, sharing and accessing ML features in production. In this talk we will touch on the key parts of this journey, the move from a virtual to a managed store, and our decision to adopt Feast. We will share our reasoning behind design decisions, the challenges we encountered, and the lessons we learned.
