ML Feature Store Basics

The "Feature Store" is the bridge between data engineering and machine learning.

The Problem

"Training-Serving Skew": When the logic used to create features for training differs from the logic used in production.

Core Pillars

• Offline Store: For batch training on historical data.
• Online Store: Low-latency storage for real-time inference.
• Registry: A central catalog of feature definitions.

Tools

• Feast: The most popular open-source feature store.
• Tecton: A managed, enterprise-grade feature platform.

Best Practices

• Point-in-Time Correctness: Prevent leakage by only joining features as they existed at a specific timestamp.
• Reusability: Shared features across multiple models.