Machine Learning Systems by Reddi
Machine Learning Systems presents a comprehensive approach to understanding and engineering machine learning (ML). While many resources focus on ML algorithms and model architectures, this book serves as a bridge between theoretical foundations and practical engineering. It emphasizes the systems context that engineers need to master when building AI solutions in the real world. The text progresses from foundational concepts to advanced system design, integrating topics such as data engineering, model optimization, hardware-aware training approaches, and inference acceleration strategies. Throughout the book, readers develop a principled understanding of ML systems engineering, learning to reason about system architectures and address critical challenges in areas including security, privacy, and reliability. While ML applications and tools evolve rapidly, the engineering principles for building ML systems remain largely consistent. This book distills these enduring concepts, making it a resource for anyone seeking to build flexible, efficient, and robust ML systems.
