Towards Data Science is a community-powered publication that showcases work in data science, machine learning and artificial intelligence. Every day newcomers, seasoned researchers and industry practitioners publish tutorials, research notes and real-world case studies that help the field move forward.

Towards Data Science

A practical guide to building scalable ML feature engineering pipelines using Feast (feature store) and Ray (distributed compute). The article addresses two common production ML challenges: inadequate feature management (training-serving skew, lack of versioning) and high feature engineering latency from sequential computation. Using a customer purchase propensity model on the UCI Online Retail dataset as a concrete example, it walks through setting up a Feast offline feature store with PostgreSQL, parallelizing rolling-window RFM and behavioral feature computation with Ray Core's remote tasks, registering feature definitions and views, and retrieving point-in-time correct features for both training and inference. Code snippets cover ray.init/put/get patterns, @ray.remote decorators, Feast entity/feature view definitions, and feature_store.yaml configuration.

Scaling Feature Engineering Pipelines with Feast and Ray

(3) Roles of Feast and Ray in Feature Engineering