Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications
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Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they’re data dependent, with data varying wildly from one use case to the next. In this book, you’ll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements.
Author Chip Huyen, co-founder of Claypot AI, considers each design decision–such as how to process and create training data, which features to use, how often to retrain models, and what to monitor–in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references.
This book will help you tackle scenarios such as:
Engineering data and choosing the right metrics to solve a business problemAutomating the process for continually developing, evaluating, deploying, and updating modelsDeveloping a monitoring system to quickly detect and address issues your models might encounter in productionArchitecting an ML platform that serves across use casesDeveloping responsible ML systems
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Publisher : O’Reilly Media
Publication date : June 21, 2022
Edition : 1st
Language : English
Print length : 386 pages
ISBN-10 : 1098107969
ISBN-13 : 978-1098107963
Item Weight : 1.4 pounds
Dimensions : 6.9 x 0.7 x 9.1 inches
Best Sellers Rank: #16,616 in Books (See Top 100 in Books) #2 in Machine Theory (Books) #2 in Business Intelligence Tools #15 in Artificial Intelligence & Semantics
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13 reviews for Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications
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Original price was: $65.99.$40.00Current price is: $40.00.
Matthew Borack –
Great AI Engineering book
Solid book for AI Engineering with broad and in-depth coverage of each topic.
Dan&Fed –
Great read
Great read for a high level view of machine learning systems.
Shadrack Osero –
A Practical Guide to Building Scalable and Reliable Machine Learning Systems
Designing Machine Learning Systems by Chip Huyen is an essential guide for practitioners looking to bridge the gap between machine learning research and real-world applications. The book offers a comprehensive, systems-focused approach to building scalable, reliable, and efficient ML models. Huyen’s writing is clear and insightful, covering topics like data-centric AI, model deployment, monitoring, and iteration. The real-world case studies and practical examples make complex concepts accessible. Whether you’re an engineer, researcher, or data scientist, this book provides valuable insights into productionizing ML effectively. A must-read for those seeking to build robust and maintainable machine learning systems. I liked its content.
Neo –
good good
Very organized and detailed review of designing typical ML system. Helpful for preparing for interviews and actual work
Levon Gagik Yeghiazaryan –
Scratches the surface, no deep dive, wide amount of topics covered
(4.5*) Overall a good overview of the topic, very easy to read and covers almost all the topics but only scratches the surface, and almost never goes deep into details.
Parimarjan Negi –
Distills the best of the blogs and folk wisdom that ML engineers pick up over the years
I am a PhD student, and have been working to apply ML to different domains for a few years. Recently, I started working with undergrad researchers who did not have any prior experience with ML applications, besides a class or so. But, there is a lot of knowledge that is just collected over the years while debugging problems, discussing with lab mates, or through the many blog posts online. These are the kind of issues that rarely come up in classes — not just conceptual AI issues — but how to deal with data / features / efficiently store things / logging etc. In the few chapters I have read through, I found this book to be like the collecting together and unifying the best blogposts and folk wisdom for practical, day to day ML issues. There were a whole lot of things that I did not know, or was curious about, but didn’t know where to look for precise answers. But more than that, I found this book to be a perfect reference for the undergrad students I was mentoring — I have lent my copy to a couple of students for reading particular chapters, particularly on training data and feature engineering, which quickly brings them up to speed on the best practices.
Tahir –
Great intro to ML adoption for pros
As a working professional coming from an application development background, I find this book to be a very clear, systematic and holistic resource into the what and how of ML adoption.This may not be the best way to learn ML theory or tools, but it’s especially useful for technology leaders who are looking to adopt ML to do so with good understanding of the fundamentals of the technology, its place in the business and the teams and processes needed for achieving success.
Alan F. Noel –
Outstanding. Most Valuable Data Science Book I Own, By Far.
I have been working in AI off and on since 1988 and have a graduate certificate in Machine Learning. I own 28 books on various topics of data science and machine learning. This book is by far the best of all of them in its utility. The book provides a great deal of very useful information. It goes into great detail on what one needs to know about putting ML solutions into production. It is by far one of the most useful books available today regarding using ML in the real world.
Eva Garcia Martin –
Covers so so many important points of putting ML in production. Highly recommend
uysalserkan –
Poor page quality and black-white colors.
shivanshu d. –
This book is thorough and discusses every aspect of common issues/questions occurs in Data Science and MLOps environment. Definitely recommend to all data people
Khalida –
Interesting book! i enjoyed reading it
Raghu –
I got it delivered on time and the book is a nice read for anyone who wants to get into the field of Machine Learning system development.