Flipbook
Feature Store 101: Everything You Need
to Know About Feature Stores
By 2020, artificial intelligence and machine learning had reached an inflection point. Organizations in almost every industry began treating their AI/ML initiatives as production-ready, highly scalable projects, not just experiments. While their vision of AI was evolving, they realized that bringing AI to production meant that a lot of time and effort was being wasted on feature definition and extraction.
Features are individual independent variables that act as inputs for AI/ML systems. Every feature represents a measurable piece of data that can be used for analysis. Having a feature store that provides unified storage for features makes it easy to engineer and manage features at scale. It allows for simple reuse of features, feature standardization across the company, and feature consistency between offline and online models. A centralized, scalable feature store allows organizations to innovate faster and drive ML processes at scale.
Feature store is a fundamental component of the AI/ML stack, and of any robust data infrastructure. In this flipbook, you will learn what feature stores are, their benefits, use cases, concepts, methodologies, and major players on the market.
The Provectus AI team has worked with several feature stores, and we would like to share our experience and lessons learned, to help you find the best path forward.
Download the flipbook and start to explore!
Download The Flipbook
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By 2020, artificial intelligence and machine learning had reached an inflection point. Organizations in almost every industry began treating their AI/ML initiatives as production-ready, highly scalable projects, not just experiments. While their vision of AI was evolving, they realized that bringing AI to production meant that a lot of time and effort was being wasted on feature definition and extraction.
Features are individual independent variables that act as inputs for AI/ML systems. Every feature represents a measurable piece of data that can be used for analysis. Having a feature store that provides unified storage for features makes it easy to engineer and manage features at scale. It allows for simple reuse of features, feature standardization across the company, and feature consistency between offline and online models. A centralized, scalable feature store allows organizations to innovate faster and drive ML processes at scale.
Feature store is a fundamental component of the AI/ML stack, and of any robust data infrastructure. In this flipbook, you will learn what feature stores are, their benefits, use cases, concepts, methodologies, and major players on the market.
The Provectus AI team has worked with several feature stores, and we would like to share our experience and lessons learned, to help you find the best path forward.
Download the flipbook and start to explore!
