A Feature Store - what is it good for

It’s good for feature reuse in machine learning, thereby increasing data science accuracy, velocity, and visibility.

A feature store is a single interface to create, discover, and access features for model training and inference. A wholistic feature store solution containing both storage and transformation layers would ideally include:

• Ingestion - both from streams and batch jobs
• Serving - low latency single features for inference and high throughput bulk features for training
• Transformation / Aggregation logic
• Discovery - features and how to retrieve them

This session will attempt to demonstrate why a feature store is useful, review current solutions, and provide a number of tips on getting started.