Designing Machine Learning Systems
by Chip Huyen · first published 2022
78% recommend22% neutral
Why HN recommends it
Practical, production-focused introduction to designing ML systems, covering the end-to-end lifecycle beyond modeling; recommended for engineers moving into ML infrastructure and hands-on practitioners.
Summarised by Claude from recommending comments
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…Kleppman. It’s one of the best tech reference books I’ve ever read. It manages to explain SO much while requiring so little prior knowledge from readers. Another book that is relatively new that I loved was Designing Machine Learning Systems by Chip Huyen. I worked in productionizing ML systems for 3 years and this book equips you with exactly what you need to do so. It does a great job of explaining the whole ML modeling pipeline and some…
on “Ask HN: What are some of the best books you have read in 2022?” ↗Designing machine learning systems by Chip Huyen is a good resource on the subject
on “Machine Learning System Design” ↗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,…
on “Books about AI&ML for your holiday reading” ↗…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…
on “Ask HN: How to pivot to a Machine Learning engineer?” ↗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...
on “The Pragmatic Programmer for Machine Learning (2023)” ↗„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
on “Ask HN: What is the best thing you read in 2024?” ↗…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)
on “Ask HN: Go deep into AI/LLMs or just use them as tools?” ↗Some that i am looking into; these are "practical" books which do not focus on the theory/algorithms but given that they are available (library/models/whatever), how to build your apps using them; 1) Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications by Chip Huyen. 2) AI Engineering: Building Applications with Foundation Models by Chip Huyen (this is a very recent book). 3) Generative Deep…
on “AI isn't going to kill the software industry” ↗…But I like to understand more. (I am aware ML is changing fast but maybe you see some longterm patterns) 1.) What do you think are good practices in ML? 2.) Can you recommend some books? I found " Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications " and " Machine Learning Design Patterns". What do you think about them? Thank you!
on “Ask HN: What are good patters / practices in Machine Learning Development?” ↗