Machine Learning Systems provides a systematic framework for understanding and engineering machine learning (ML) systems. This textbook bridges the gap between theoretical foundations and practical engineering, emphasizing the systems perspective required to build effective AI solutions. Unlike resources that focus primarily on algorithms and model architectures, this book highlights the broader context in which ML systems operate, including data engineering, model optimization, hardware-aware training, and inference acceleration. Readers will develop the ability to reason about ML system architectures and apply enduring engineering principles for building flexible, efficient, and robust machine learning systems.

机器学习系统为理解和设计机器学习 (ML) 系统提供了一个系统框架。这本教科书弥合了理论基础和实际工程之间的差距，强调构建有效的人工智能解决方案所需的系统视角。与主要关注算法和模型架构的资源不同，本书重点介绍了机器学习系统运行的更广泛背景，包括数据工程、模型优化、硬件感知训练和推理加速。读者将培养推理机器学习系统架构的能力，并应用持久的工程原理来构建灵活、高效和强大的机器学习系统。