BeautyBooksElectronicsHome & KitchenSports & OutdoorsIndustrial & ScientificToys & Games
Deliver toUnited Arab Emirates
Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications

Description:

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 problem
  • Automating the process for continually developing, evaluating, deploying, and updating models
  • Developing a monitoring system to quickly detect and address issues your models might encounter in production
  • Architecting an ML platform that serves across use cases
  • Developing responsible ML systems
Read more

Editorial Reviews

Review

"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!

Reviews:

5.0 out of 5 stars good good

N. · August 18, 2025

(function() { P.when('cr-A', 'ready').execute(function(A) { if(typeof A.toggleExpanderAriaLabel === 'function') { A.toggleExpanderAriaLabel('review_text_read_more', 'Read more of this review', 'Read less of this review'); } }); })(); .review-text-read-more-expander:focus-visible { outline: 2px solid #2162a1; outline-offset: 2px; border-radius: 5px; } Very organized and detailed review of designing typical ML system. Helpful for preparing for interviews and actual work

5.0 out of 5 stars A Practical Guide to Building Scalable and Reliable Machine Learning Systems

S.O. · February 2, 2025

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.

5.0 out of 5 stars Great intro to ML adoption for pros

T. · October 20, 2023

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.

5.0 out of 5 stars Excellent reference for aspiring ML architects

J.W. · March 24, 2023

Every chapter is worth reading. And, this book does a fantastic job making hard concepts more consumable. I found the insights on data shifting and ML Ops to be particularly useful and this will be my goto reference until the next edition is released. Chip did a really good job with this book. She clearly knows her stuff. Lastly, the illustrations were excellent. This is a near perfect book. The only flaw is that she stopped at 11 chapters.

4.0 out of 5 stars Scratches the surface, no deep dive, wide amount of topics covered

L.G.Y. · January 23, 2025

(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.

5.0 out of 5 stars Distills the best of the blogs and folk wisdom that ML engineers pick up over the years

P.N. · July 2, 2022

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.

5.0 out of 5 stars Outstanding. Most Valuable Data Science Book I Own, By Far.

A.F.N. · June 15, 2023

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.

3.0 out of 5 stars Great author bad quality product

T.S. · August 23, 2025

Boook is great but the seller gave it to me kinda damaged and the sheets paper quality is low

10/10 fantastic book

E.G.M. · February 24, 2025

(function() { P.when('cr-A', 'ready').execute(function(A) { if(typeof A.toggleExpanderAriaLabel === 'function') { A.toggleExpanderAriaLabel('review_text_read_more', 'Read more of this review', 'Read less of this review'); } }); })(); .review-text-read-more-expander:focus-visible { outline: 2px solid #2162a1; outline-offset: 2px; border-radius: 5px; } Covers so so many important points of putting ML in production. Highly recommend

Comes with black white colors

u. · October 3, 2024

Poor page quality and black-white colors.

Good book

K. · April 16, 2025

Interesting book! i enjoyed reading it

A must read for an ML enthusiast

R. · September 29, 2025

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.

Go-to reference for AI pipelines insights.

M. · August 18, 2025

Very well written and enjoyable technical book.Whether you already work in this domain, want a refresher, or simply clarify some topics that are outside of your day-to-day duties, this book won't disappoint.

Visit the Chip Huyen (Author) Store

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

Product ID: U1098107969
Condition: New

4.6

AED29703

Price includes VAT & Import Duties
Type: Paperback
Availability: In Stock

Quantity:

|

Order today to get by 7-14 business days

This item qualifies for free delivery

Returns & Warranty policies

Imported From: United States

At BOLO, we work hard to ensure the products you receive are new, genuine, and sourced from reputable suppliers.

BOLO is not an authorized or official retailer for most brands, nor are we affiliated with manufacturers unless specifically stated on a product page. Instead, we source verified sellers, authorized distributors or directly from the manufacturer.

Each product undergoes thorough inspection and verification at our consolidation and fulfilment centers to ensure it meets our strict authenticity and quality standards before being shipped and delivered to you.

If you ever have concerns regarding the authenticity of a product purchased from us, please contact Bolo Support. We will review your inquiry promptly and, if necessary, provide documentation verifying authenticity or offer a suitable resolution.

Your trust is our top priority, and we are committed to maintaining transparency and integrity in every transaction.

All product information, images, descriptions, and reviews originate from the manufacturer or from trusted sellers overseas. BOLO is not affiliated with, endorsed by, or an authorized retailer for most brands listed on our website unless stated otherwise.

While we strive to display accurate information, variations in packaging, labeling, instructions, or formulation may occasionally occur due to regional differences or supplier updates. For detailed or manufacturer-specific information, please contact the brand directly or reach out to BOLO Support for assistance.

Unless otherwise stated, all prices displayed on the product page include applicable taxes and import duties.

BOLO operates in accordance with the laws and regulations of United Arab Emirates. Any items found to be restricted or prohibited for sale within the UAE will be cancelled prior to shipment. We take proactive measures to ensure that only products permitted for sale in United Arab Emirates are listed on our website.

All items are shipped by air, and any products classified as “Dangerous Goods (DG)” under IATA regulations will be removed from the order and cancelled.

All orders are processed manually, and we make every effort to process them promptly once confirmed. Products cancelled due to the above reasons will be permanently removed from listings across the website.

Similar suggestions by Bolo

More from this brand

Similar items from “Intelligence & Semantics”

Share with

Or share with link

https://www.bolo.ae/products/U1098107969

Visit the Chip Huyen (Author) Store

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

Product ID: U1098107969
Condition: New

4.6

Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications-0
Type: Paperback

AED29703

Price includes VAT & Import Duties
Availability: In Stock

Quantity:

|

Order today to get by 7-14 business days

This item qualifies for free delivery

Returns & Warranty policies

Imported From: United States

At BOLO, we work hard to ensure the products you receive are new, genuine, and sourced from reputable suppliers.

BOLO is not an authorized or official retailer for most brands, nor are we affiliated with manufacturers unless specifically stated on a product page. Instead, we source verified sellers, authorized distributors or directly from the manufacturer.

Each product undergoes thorough inspection and verification at our consolidation and fulfilment centers to ensure it meets our strict authenticity and quality standards before being shipped and delivered to you.

If you ever have concerns regarding the authenticity of a product purchased from us, please contact Bolo Support. We will review your inquiry promptly and, if necessary, provide documentation verifying authenticity or offer a suitable resolution.

Your trust is our top priority, and we are committed to maintaining transparency and integrity in every transaction.

All product information, images, descriptions, and reviews originate from the manufacturer or from trusted sellers overseas. BOLO is not affiliated with, endorsed by, or an authorized retailer for most brands listed on our website unless stated otherwise.

While we strive to display accurate information, variations in packaging, labeling, instructions, or formulation may occasionally occur due to regional differences or supplier updates. For detailed or manufacturer-specific information, please contact the brand directly or reach out to BOLO Support for assistance.

Unless otherwise stated, all prices displayed on the product page include applicable taxes and import duties.

BOLO operates in accordance with the laws and regulations of United Arab Emirates. Any items found to be restricted or prohibited for sale within the UAE will be cancelled prior to shipment. We take proactive measures to ensure that only products permitted for sale in United Arab Emirates are listed on our website.

All items are shipped by air, and any products classified as “Dangerous Goods (DG)” under IATA regulations will be removed from the order and cancelled.

All orders are processed manually, and we make every effort to process them promptly once confirmed. Products cancelled due to the above reasons will be permanently removed from listings across the website.

Description:

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 problem
  • Automating the process for continually developing, evaluating, deploying, and updating models
  • Developing a monitoring system to quickly detect and address issues your models might encounter in production
  • Architecting an ML platform that serves across use cases
  • Developing responsible ML systems
Read more

Editorial Reviews

Review

"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!

Reviews:

5.0 out of 5 stars good good

N. · August 18, 2025

(function() { P.when('cr-A', 'ready').execute(function(A) { if(typeof A.toggleExpanderAriaLabel === 'function') { A.toggleExpanderAriaLabel('review_text_read_more', 'Read more of this review', 'Read less of this review'); } }); })(); .review-text-read-more-expander:focus-visible { outline: 2px solid #2162a1; outline-offset: 2px; border-radius: 5px; } Very organized and detailed review of designing typical ML system. Helpful for preparing for interviews and actual work

5.0 out of 5 stars A Practical Guide to Building Scalable and Reliable Machine Learning Systems

S.O. · February 2, 2025

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.

5.0 out of 5 stars Great intro to ML adoption for pros

T. · October 20, 2023

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.

5.0 out of 5 stars Excellent reference for aspiring ML architects

J.W. · March 24, 2023

Every chapter is worth reading. And, this book does a fantastic job making hard concepts more consumable. I found the insights on data shifting and ML Ops to be particularly useful and this will be my goto reference until the next edition is released. Chip did a really good job with this book. She clearly knows her stuff. Lastly, the illustrations were excellent. This is a near perfect book. The only flaw is that she stopped at 11 chapters.

4.0 out of 5 stars Scratches the surface, no deep dive, wide amount of topics covered

L.G.Y. · January 23, 2025

(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.

5.0 out of 5 stars Distills the best of the blogs and folk wisdom that ML engineers pick up over the years

P.N. · July 2, 2022

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.

5.0 out of 5 stars Outstanding. Most Valuable Data Science Book I Own, By Far.

A.F.N. · June 15, 2023

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.

3.0 out of 5 stars Great author bad quality product

T.S. · August 23, 2025

Boook is great but the seller gave it to me kinda damaged and the sheets paper quality is low

10/10 fantastic book

E.G.M. · February 24, 2025

(function() { P.when('cr-A', 'ready').execute(function(A) { if(typeof A.toggleExpanderAriaLabel === 'function') { A.toggleExpanderAriaLabel('review_text_read_more', 'Read more of this review', 'Read less of this review'); } }); })(); .review-text-read-more-expander:focus-visible { outline: 2px solid #2162a1; outline-offset: 2px; border-radius: 5px; } Covers so so many important points of putting ML in production. Highly recommend

Comes with black white colors

u. · October 3, 2024

Poor page quality and black-white colors.

Good book

K. · April 16, 2025

Interesting book! i enjoyed reading it

A must read for an ML enthusiast

R. · September 29, 2025

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.

Go-to reference for AI pipelines insights.

M. · August 18, 2025

Very well written and enjoyable technical book.Whether you already work in this domain, want a refresher, or simply clarify some topics that are outside of your day-to-day duties, this book won't disappoint.

Similar suggestions by Bolo

More from this brand

Similar items from “Intelligence & Semantics”

Share with

Or share with link

https://www.bolo.ae/products/U1098107969