Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications 1st Edition

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ISBN-13: 978-1098107963
ISBN-10: 1098107969
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From the brand

From the Publisher

You might be able to relate to one of the following scenarios:

  • You have been given a business problem and a lot of raw data. You want to engineer this data and choose the right metrics to solve this problem.
  • Your initial models perform well in offline experiments and you want to deploy them. You have little feedback on how your models are performing after your models are deployed, and you want to figure out a way to quickly detect, debug, and address any issue your models might run into in production.
  • The process of developing, evaluating, deploying, and updating models for your team has been mostly manual, slow, and error-prone. You want to automate and improve this process.
  • Each ML use case in your organization has been deployed using its own workflow, and you want to lay down the foundation (e.g., model store, feature store, monitoring tools) that can be shared and reused across use cases.
  • You’re worried that there might be biases in your ML systems and you want to make your systems responsible!

You can also benefit from the book if you belong to one of the following groups:

  • Tool developers who want to identify underserved areas in ML production and figure out how to position your tools in the ecosystem.
  • Individuals looking for ML-related roles in the industry.
  • Technical and business leaders who are considering adopting ML solutions to improve your products and/or business processes. Readers without strong technical backgrounds might benefit the most from Chapters 1, 2, and 11.

What This Book Is Not

This book is not an introduction to ML. There are many books, courses, and resources available for ML theories, and therefore, this book shies away from these concepts to focus on the practical aspects of ML. To be specific, the book assumes that readers have a basic understanding of the following topics:

  • ML models such as clustering, logistic regression, decision trees, collaborative filtering, and various neural network architectures including feed-forward, recurrent, convolutional, and transformer
  • ML techniques such as supervised versus unsupervised, gradient descent, objective/loss function, regularization, generalization, and hyperparameter tuning
  • Metrics such as accuracy, F1, precision, recall, ROC, mean squared error, and log-likelihood
  • Statistical concepts such as variance, probability, and normal/long-tail distribution
  • Common ML tasks such as language modeling, anomaly detection, object classification, and machine translation

You don’t have to know these topics inside out—for concepts whose exact definitions can take some effort to remember, e.g., F1 score, we include short notes as references—but you should have a rough sense of what they mean going in.

While this book mentions current tools to illustrate certain concepts and solutions, it’s not a tutorial book. Technologies evolve over time. Tools go in and out of style quickly, but fundamental approaches to problem solving should last a bit longer. This book provides a framework for you to evaluate the tool that works best for your use cases. When there’s a tool you want to use, it’s usually straightforward to find tutorials for it online. As a result, this book has few code snippets and instead focuses on providing a lot of discussion around trade-offs, pros and cons, and concrete examples.

Editorial Reviews


"This is, simply, the very best book you can read about how to build, deploy, and scale machine learning models at a company for maximum impact. Chip is a masterful teacher, and the breadth and depth of her knowledge is unparalleled."

- Josh Wills, Software Engineer at WeaveGrid and former Director of Data Engineering, Slack

There is so much information one needs to know to be an effective machine learning engineer. It's hard to cut through the chaff to get the most relevant information, but Chip has done that admirably with this book. If you are serious about ML in production, and care about how to design and implement ML systems end to end, this book is essential."

- Laurence Moroney, AI and ML Lead, Google

"One of the best resources that focuses on the first principles behind designing ML systems for production. A must-read to navigate the ephemeral landscape of tooling and platform options."
- Goku Mohandas, Founder of Made With ML

"Chip's manual is the book we deserve and the one we need right now. In a blooming but chaotic ecosystem, this principled view on end-to-end ML is both your map and your compass: a must-read for practitioners inside and outside of Big Tech—especially those working at 'reasonable scale.' This book will also appeal to data leaders looking for best practices on how to deploy, manage, and monitor systems in the wild."
- Jacopo Tagliabue, Director of AI, Coveo; Adj. Professor of MLSys, NYU

"Chip is truly a world-class expert on machine learning systems, as well as a brilliant writer. Both are evident in this book, which is a fantastic resource for anyone looking to learn about this topic."
- Andrey Kurenkov, PhD Candidate at the Stanford AI Lab

From the Author

Ever since the first machine learning course I taught at Stanford in 2017, many people have asked me for advice on how to deploy ML models at their organizations. These questions can be generic, such as "What model should I use?" "How often should I retrain my model?" "How can I detect data distribution shifts?" "How do I ensure that the features used during training are consistent with the features used during inference?"
These questions can also be specific, such as "I'm convinced that switching from batch prediction to online prediction will give our model a performance boost, but how do I convince my manager to let me do so?" or "I'm the most senior data scientist at my company and I've recently been tasked with setting up our first machine learning platform; where do I start?"
My short answer to all these questions is always: "It depends." My long answers often involve hours of discussion to understand where the questioner comes from, what they're actually trying to achieve, and the pros and cons of different approaches for their specific use case.
ML systems are both complex and unique. They are complex because they consist of many different components (ML algorithms, data, business logics, evaluation metrics, underlying infrastructure, etc.) and involve many different stakeholders (data scientists, ML engineers, business leaders, users, even society at large). ML systems are unique because they are data dependent, and data varies wildly from one use case to the next.
For example, two companies might be in the same domain (ecommerce) and have the same problem that they want ML to solve (recommender system), but their resulting ML systems can have different model architecture, use different sets of features, be evaluated on different metrics, and bring different returns on investment.
Many blog posts and tutorials on ML production focus on answering one specific question. While the focus helps get the point across, they can create the impression that it's possible to consider each of these questions in isolation. In reality, changes in one component will likely affect other components. Therefore, it's necessary to consider the system as a whole while attempting to make any design decision.
This book takes a holistic approach to ML systems. It takes into account different components of the system and the objectives of different stakeholders involved. The content in this book is illustrated using actual case studies, many of which I've personally worked on, backed by ample references, and reviewed by ML practitioners in both academia and industry. Sections that require in-depth knowledge of a certain topic—e.g., batch processing versus stream processing, infrastructure for storage and compute, and responsible AI—are further reviewed by experts whose work focuses on that one topic. In other words, this book is an attempt to give nuanced answers to the questions mentioned above and more.
When I first wrote the lecture notes that laid the foundation for this book, I thought I wrote them for my students to prepare them for the demands of their future jobs as data scientists and ML engineers. However, I soon realized that I also learned tremendously through the process. The initial drafts I shared with early readers sparked many conversations that tested my assumptions, forced me to consider different perspectives, and introduced me to new problems and new approaches.

I hope that this learning process will continue for me now that the book is in your hand, as you have experiences and perspectives that are unique to you. Please feel free to share with me any feedback you might have for this book!

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Product details

  • Publisher ‏ : ‎ O'Reilly Media; 1st edition (June 21, 2022)
  • Language ‏ : ‎ English
  • Paperback ‏ : ‎ 386 pages
  • ISBN-10 ‏ : ‎ 1098107969
  • ISBN-13 ‏ : ‎ 978-1098107963
  • Item Weight ‏ : ‎ 1.36 pounds
  • Dimensions ‏ : ‎ 7 x 0.75 x 9 inches
  • Customer Reviews:
    4.7 out of 5 stars 133 ratings

About the author

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I’m Chip Huyen, a writer and computer scientist. I grew up chasing grasshoppers in a small rice-farming village in Vietnam.

I’m a co-founder of Claypot AI, a platform for real-time machine learning. Previously, I built machine learning tools at NVIDIA, Snorkel AI, Netflix, and Primer.

I graduated from Stanford University, where I currently teach CS 329S: Machine Learning Systems Design. I’m also the author of the book Designing Machine Learning Systems (O’Reilly, 2022).

LinkedIn included me among Top Voices in Software Development (2019) and Top Voices in Data Science & AI (2020).

In my free time, I travel and write. After high school, I went to Brunei for a 3-day vacation which turned into a 3-year trip through Asia, Africa, and South America. During my trip, I worked as a Bollywood extra, a casino hostess, and a street performer.

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5.0 out of 5 stars Practical aspects of machine learning
By Ananda Soundhararajan on July 11, 2022
Chip has delivered a great content to understand the practical & operational aspects of machine learning systems which we can't find it in MOOC platforms. This is a must read if you are passionate about designing reliable ML systems.
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