Scaling Feature Engineering Pipelines with Feast and Ray | Towards Data Science

Towards Data Science

by Kenneth Leung

February 25, 2026

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Scaling feature engineering pipelines effectively is a critical challenge in machine learning, particularly when dealing with large-scale datasets and complex computations. In this article, the author addresses common issues faced in feature engineering, such as inadequate feature management, inconsistent feature definitions between training and inference, and high computational latency due to sequential processing of heavy workloads. These problems often arise in production ML systems, where time-series data requires multiple window-based transformations, leading to inefficiencies and scalability bottlenecks. To tackle these challenges, the author introduces Feast, an open-source feature store designed to centralize feature management, enforce consistency between training and serving data, and enable cross-team collaboration. Feast ensures point-in-time correctness by preventing data leakage and providing a single source of truth for features. Additionally, Ray, a distributed compute framework, is used to optimize heavy feature engineering tasks by enabling parallel execution of computations, significantly reducing latency. The article demonstrates the integration of Feast and Ray through an example use case involving the UCI Online Retail dataset, where the goal is to build a 30-day customer purchase propensity model. The approach involves computing features like recency, frequency, monetary value (RFM), and other behavioral metrics using a rolling window design. This method allows for consistent feature generation across different time windows, ensuring accurate predictions while managing computational efficiency. By leveraging Feast and Ray, the author highlights how organizations can streamline their feature engineering workflows, improve reproducibility, and scale their ML pipelines effectively. This approach is particularly valuable for AI practitioners looking to deploy robust, scalable machine learning systems in production environments. The combination of a centralized feature store and distributed computing framework not only enhances efficiency but also ensures consistency and reliability in model training and inference, making it a powerful solution for modern AI challenges.

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Originally published on Towards Data Science on 2/25/2026