Machine Learning Systems
Principles and Practices of Engineering Artificially Intelligent Systems
Abstract
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.
Coming soon in 2026!
Publisher: MIT Press
AI Engineering for All
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Why We Wrote This Book
This grew out of a concern that while students are eager to train AI models and become AI programmers, few understand how to build the systems that actually make them work. Even when ML systems concepts were taught, students often learned individual components without grasping the holistic architecture—they could see the trees but missed the forest. As AI becomes more capable and autonomous, the critical bottleneck won’t be the algorithms—it will be the AI engineers who can build efficient, scalable, and sustainable systems that safely harness that intelligence.
"If you want to go fast, go alone. If you want to go far, go together."
This vision emerged from collaborative work in CS249r at Harvard University, where students, faculty, and industry partners came together to explore the systems side of machine learning. The initial course materials were developed through structured homework assignments during the Fall 2023 semester, with students contributing to the collective development of the content. This collaborative foundation has since evolved into a comprehensive educational resource that we wish to share globally.
Our vision for this book and its broader mission is deeply rooted in the transformative potential of AI and the need to make AI education globally accessible to all. To learn more about the inspiration behind this project and the values driving its creation, we encourage you to read the Author’s Note.
Podcast
🎧 AI-Generated Podcast Overview
This short podcast, created with Google's Notebook LM and inspired by insights from our IEEE education viewpoint paper, offers an accessible overview of the book's key ideas and themes.
Global Outreach
Thank you to all our readers and visitors. Your engagement with the material keeps us motivated.
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