📄 中文摘要
特征存储系统如 Feast 和分布式计算框架 Ray 在生产机器学习系统中的应用越来越广泛。Feast 提供了一个集中式的特征存储解决方案，能够高效管理和共享特征，而 Ray 则通过其强大的分布式计算能力，支持大规模数据处理和实时特征计算。这两者的结合使得特征工程管道能够更好地扩展，提升机器学习模型的训练和推理效率。通过使用 Feast 和 Ray，数据科学家可以更快速地迭代特征，优化模型性能，最终实现更高效的机器学习工作流。
利用 Feast 和 Ray 扩展特征工程管道
出处: Scaling Feature Engineering Pipelines with Feast and Ray
发布: 2026年2月25日
📄 English Summary
Scaling Feature Engineering Pipelines with Feast and Ray
Feature stores like Feast and distributed computing frameworks like Ray are increasingly utilized in production machine learning systems. Feast offers a centralized feature storage solution that efficiently manages and shares features, while Ray provides powerful distributed computing capabilities for large-scale data processing and real-time feature computation. The combination of these two technologies enhances the scalability of feature engineering pipelines, improving the training and inference efficiency of machine learning models. By leveraging Feast and Ray, data scientists can iterate on features more rapidly, optimize model performance, and ultimately achieve a more efficient machine learning workflow.