Managing Reusable Features with H2O Feature Store

This lesson introduces H2O Feature Store as a way to store, update, retrieve, and share features used across machine learning projects. It covers searching for existing features by metadata tags and data type, reusing feature sets derived from prior transformations or automatic feature engineering, and choosing between online and offline stores depending on whether you need large-scale or low-latency retrieval. It also covers permissions on feature sets, which restrict access to authorized users for sensitive data, and shows browsing public projects, such as one aimed at cutting telecom customer churn by 20 percent.