Machine Learning Systems

Preface

Welcome to Machine Learning Systems, your gateway to the fast-paced world of machine learning (ML) systems. This book is an extension of the CS249r course at Harvard University, taught by Prof. Vijay Janapa Reddi, and is the result of a collaborative effort involving students, professionals, and the broader community of AI practitioners.

We’ve created this open-source book to demystify the process of building efficient and scalable ML systems. Our goal is to provide a comprehensive guide that covers the principles, practices, and challenges of developing robust ML pipelines for deployment. This isn’t a static textbook—it’s a living, evolving resource designed to keep pace with advancements in the field.

“If you want to go fast, go alone. If you want to go far, go together.” – African Proverb

As a living and breathing resource, this book is a continual work in progress, reflecting the ever-evolving nature of machine learning systems. Advancements in the ML landscape drive our commitment to keeping this resource updated with the latest insights, techniques, and best practices. We warmly invite you to join us on this journey by contributing your expertise, feedback, and ideas.

Global Outreach

Thank you to all our readers and visitors. Your engagement with the material keeps us motivated.

Why We Wrote This Book

While there are plenty of resources that focus on the algorithmic side of machine learning, resources on the systems side of things are few and far between. This gap inspired us to create this book—a resource dedicated to the principles and practices of building efficient and scalable ML systems.

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.

Want to Help Out?

This is a collaborative project, and your input matters! If you’d like to contribute, check out our contribution guidelines. Feedback, corrections, and new ideas are welcome—simply file a GitHub issue.

What’s Next?

If you’re ready to dive deeper into the book’s structure, learning objectives, and practical use, visit the About the Book section for more details.