Feature Store 101: Everything You Need to Know About Feature Stores
Why feature stores became table stakes for production ML — and how to adopt one.
By 2020, AI and ML had hit an inflection point. Organizations across nearly every industry stopped treating AI/ML as experimental work and started running it as production-grade, highly scalable infrastructure. As that vision matured, teams kept hitting the same wall: too much time spent on feature definition and extraction during production rollouts.
A feature store solves that. It provides unified storage for the individual variables that feed AI/ML systems, so the same features can be reused, standardized, and kept consistent between offline training and online inference. It is now a fundamental component of any robust AI/ML stack.
This flipbook walks through:
- What feature stores are and why they became table stakes for production ML
- The benefits — reuse, standardization, offline/online consistency, engineering at scale
- Real use cases, core concepts, and methodologies
- The major market players and how they compare
- Lessons learned from the Provectus AI team to help you chart the best adoption path