Name: Designing Machine Learning Systems
ISBN: 9781098107963

Designing Machine Learning Systems

Many tutorials show you how to develop ML systems from ideation to deployed models. But with constant changes in tooling, those systems can quickly become outdated. Without an intentional design to hold the components together, these systems will become a technical liability, prone to errors and be quick to fall apart. In this book, Chip Huyen provides a framework for designing real-world ML systems that are quick to deploy, reliable, scalable, and iterative. These systems have the capacity to learn from new data, improve on past mistakes, and adapt to changing requirements and environments. YouÃ??Ã?Â¢??ll learn everything from project scoping, data management, model development, deployment, and infrastructure to team structure and business analysis. Learn the challenges and requirements of an ML system in production Build training data with different sampling and labeling methods Leverage best techniques to engineer features for your ML models to avoid data leakage Select, develop, debug, and evaluate ML models that are best suit for your tasks Deploy different types of ML systems for different hardware Explore major infrastructural choices and hardware designs Understand the human side of ML, including integrating ML into business, user experience, and team structure

What Hacker News says

namnnumbrMay 2025

For SWEs interested in "AI Engineering" (either getting involved in how models work, or building applications on them), there's a critical paradigm shift in that using "AI" requires more of an experimental mindset than software engineering typically does.

- I strongly recommend Chip Huyen's books ("Designing Machine Learning Systems" and "AI Engineering") and blog (https://huyenchip.com/blog/).

- Andreessen Horowitz' "AI Cannon" is a good reference listicle (https://a16z.com/ai-canon/)

- "12 factor agents" (https://github.com/humanlayer/12-factor-agents)

thread

yu3zhou4Dec 2024

„Understanding Deep Learning” by Simon Prince

Among all ML books, it’s a really good overview of many concepts connected one to another and spelled out in easy to grasp way

Also „Designing Machine Learning Systems” by Chip Huyen looks really promising and very hands-on (lessons learned by very committed practitioner). It gives actionable tips and provides real life insights. I haven’t finished reading it yet though

thread

vismit2000Sep 2024

A good related book is 'Designing Machine Learning Systems' by Chip Huyen which covers similar topics: https://www.oreilly.com/library/view/designing-machine-learn...

thread

arnabghoJun 2024

I was a MLE Tech Lead at Snap and laid some of the foundations of the generative AI infra at Snap. I would highly recommend MLE route as a very rewarding career path.

This book is a very good introduction to designing Machine Learning Systems for production: https://www.amazon.com/Designing-Machine-Learning-Systems-Pr...

This blog by the same author is highly recommended as an intro into building production grade AI and ML systems: https://huyenchip.com/2023/04/11/llm-engineering.html

To summarize answers to your questions:

(1) Yes it is wise to do this transition especially at an inflection point in the zeitgeist of the times as now

(2) Yes

(3) See above for resources on how to get started and reach mastery in the craft of ML Engineering.

thread

kavaivaleriDec 2023

Classics

- Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

- The Book of Why: The New Science of Cause and Effect by Judea Pearl, Dana Mackenzie

- Designing Machine Learning Systems by Chip Huyen

- The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World by Pedro Domingo

Fresh look (books published in 2023)

- The Worlds I See: Curiosity, Exploration, and Discovery at the Dawn of AI by Fei-Fei Li

- The Coming Wave by Mustafa Suleyman and Michael Bhaskar

- Understanding Deep Learning by Simon J. D. Prince

History of Artificial Intelligence and Machine Learning

- The Turing Guide by Jack Copeland, Jonathan Bowen, Mark Sprevak, Robin Wilson

- The Quest for Artificial Intelligence: A History of Ideas and Achievements by Nils. J Nilsson

- Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence by Pamela McCorduck

AI and Future Studies

- The Year in Tech, 2023: The Insights You Need from Harvard Business Review (HBR Insights Series) by Harvard Business Review, Beena Ammanath, Andrew Ng, Michael Luca, and Bhaskar Ghosh

- AI 2041: Ten Visions for Our Future by Kai-Fu Lee and Chen Qiufan

- The Age of AI: And Our Human Future by Henry Kissinger, Eric Schmidt, and Daniel Huttenlocher

AI ethics collection

- AI: Its nature and future by Margaret Boden

- AI Ethics by Mark Coeckelbergh

- The Costs of Connection: How Data Is Colonizing Human Life and Appropriating It for Capitalism by Nick Couldry, Ulises Mejias

- Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence by Kate Crawford

Lastly, a fire-side list of books written completely using AI

- Aum Golly: Poems on Humanity by an Artificial Intelligence by Jukka Aalho

- Aum Golly 2: Illustrated Poems on Humanity by Artificial Intelligence by Jukka Aalho

- 1 the Road by Ross Goodwin

- The Inner Life of an AI: A Memoir by ChatGPT by Forrest Xiao