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Andriy Burkov

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Machine Learning Engineering

by Andriy Burkov (Author)

4.7 262 ratings

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From the author of a world bestseller published in eleven languages, The Hundred-Page Machine Learning Book, this new book by Andriy Burkov is the most complete applied AI book out there. It is filled with best practices and design patterns of building reliable machine learning solutions that scale. Andriy Burkov has a Ph.D. in AI and is the leader of a machine learning team at Gartner. This book is based on Andriy's own 15 years of experience in solving problems with AI as well as on the published experience of the industry leaders.

Here's what Cassie Kozyrkov, Chief Decision Scientist at Google tells about the book in the Foreword:

"You're looking at one of the few true Applied Machine Learning books out there. That's right, you found one! A real applied needle in the haystack of research-oriented stuff. Excellent job, dear reader... unless what you were actually looking for is a book to help you learn the skills to design general-purpose algorithms, in which case I hope the author won't be too upset with me for telling you to flee now and go pick up pretty much any other machine learning book. This one is different."
[...]
"So, what's in [...] the book? The machine learning equivalent of a bumper guide to innovating in recipes to make food at scale. Since you haven't read the book yet, I'll put it in culinary terms: you'll need to figure out what's worth cooking / what the objectives are (decision-making and product management), understand the suppliers and the customers (domain expertise and business acumen), how to process ingredients at scale (data engineering and analysis), how to try many different ingredient-appliance combinations quickly to generate potential recipes (prototype phase ML engineering), how to check that the quality of the recipe is good enough to serve (statistics), how to turn a potential recipe into millions of dishes served efficiently (production phase ML engineering), and how to ensure that your dishes stay top-notch even if the delivery truck brings you a ton of potatoes instead of the rice you ordered (reliability engineering). This book is one of the few to offer perspectives on each step of the end-to-end process."
[...]
"One of my favorite things about this book is how fully it embraces the most important thing you need to know about machine learning: mistakes are possible... and sometimes they hurt. As my colleagues in site reliability engineering love to say, "Hope is not a strategy." Hoping that there will be no mistakes is the worst approach you can take. This book does so much better. It promptly shatters any false sense of security you were tempted to have about building an AI system that is more "intelligent" than you are. (Um, no. Just no.) Then it diligently takes you through a survey of all kinds of things that can go wrong in practice and how to prevent/detect/handle them. This book does a great job of outlining the importance of monitoring, how to approach model maintenance, what to do when things go wrong, how to think about fallback strategies for the kinds of mistakes you can't anticipate, how to deal with adversaries who try to exploit your system, and how to manage the expectations of your human users (there's also a section on what to do when your, er, users are machines). These are hugely important topics in practical machine learning, but they're so often neglected in other books. Not here."

"If you intend to use machine learning to solve business problems at scale, I'm delighted you got your hands on this book. Enjoy!"

ISBN-10

1999579577
ISBN-13

978-1999579579
Publication date

September 5, 2020
Language

English
Dimensions

7.5 x 0.73 x 9.25 inches
Print length

310 pages
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Editorial Reviews

About the Author

Andriy Burkov holds a Ph.D. in Artificial Intelligence. He works as a senior data scientist and machine learning team leader at Gartner.

Product details

Publisher ‏ : ‎ True Positive Inc. (September 5, 2020)
Language ‏ : ‎ English
Paperback ‏ : ‎ 310 pages
ISBN-10 ‏ : ‎ 1999579577
ISBN-13 ‏ : ‎ 978-1999579579
Item Weight ‏ : ‎ 1.18 pounds
Dimensions ‏ : ‎ 7.5 x 0.73 x 9.25 inches

Best Sellers Rank: #342,587 in Books (See Top 100 in Books)
- #51 in Machine Theory (Books)
- #57 in Computer Vision & Pattern Recognition
- #597 in Artificial Intelligence & Semantics

Customer Reviews:
4.7 262 ratings

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Andriy Burkov

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Andriy Burkov is a dad of two and a machine learning expert based in Quebec City, Canada. Eleven years ago, he got a Ph.D. in Artificial Intelligence, and for the last eight years, he's been leading a team of machine learning developers at Gartner.

His specialty is natural language processing. His team works on building state-of-the-art multilingual text extraction and normalization systems for production, using both shallow and deep learning technologies.

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4.7 out of 5

262 global ratings

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Glenn Schultz

An exceptional follow through on the 100 page Machine Learning Book

Reviewed in the United States on January 18, 2021

Verified Purchase

This book is an exceptional follow through on the part of the author of the 100 page machine learning book. He covers the 'engineering' of machine learning from start to finish. The 100 page machine learning book introduces the reader to machine learning algorithms and the 'math' behind the magic. However, deploying a machine learning solution is much more than the model. The author clearly outlines the principles once must understand to successfully deploy a machine learning solution.

I particularly enjoyed Sections 1.4 and 1.5 when to use and when not to use machine learning. From the discussion one can clearly set forth the criteria establishing when one should pursue a machine learning solution and when one should pursue other alternatives. A brief stop in each section will undoubtedly save many both valuable time and frustration.

Overall, an excellent work. If you are interested in machine learning I highly recommend this book as well as 'The 100 Page Machine Learning Book.'

3 people found this helpful

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Vinny Meller

Extremely valuable

Reviewed in the United States on October 12, 2020

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I graduated 11 months ago and have been working as a Data Scientist since. Finishing this book makes me feel like I've worked on orders of magnitude more projects than I have. The examples are very clear and you're always left feeling like you understand WHY each topic is important. Not only did this book teach me new things about MLE but it helped cement things I already figured out for myself on the job. More often than not, Andriy's words are an invaluable reinforcement for concepts I thought I already knew. Highly recommended.

4 people found this helpful

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Cardec

The one book you definitely want to read and keep in your collection

Reviewed in the United States on June 5, 2021

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Let me be very clear and to the point. If you like me, don’t like to have too many volumes laying around, listen you will not regret these master piece. The author did an amazing job keeping it concise while at the same time capturing depth and broadness of each selected data engineering topic. I only rank it 5 stars because the scale doesn’t provide for more.

2 people found this helpful

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hk1995

Must Read without any doubts!

Reviewed in the United States on September 18, 2020

Verified Purchase

This is a must read for any data scientist looking to transition to a ML engineer role. I have over 3 years of experience in data science at a leading financial services firm and I must say this book has taught me so many new things. This is a treasure of gold written by Andriy. I have read the 100 pages ML book too. Both of his books are a must read and can be a good daily reference.

One person found this helpful

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Jesper Nordström

A much needed book

Reviewed in the United States on December 5, 2020

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For an aspiring data scientist wanting to learn how to develop models, there has been no shortage of resources. But learning how to manage the ML lifecycle and put the models into production has been the real challenge. Andriy Burkov follows up his previous great ”The 100 page Machine Learning Book” with this excellent new book. Highly recommended.

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Amazon Customer

A must read for anyone interested in Applied Machine Learning

Reviewed in the United States on November 11, 2020

Verified Purchase

It is an excellent read for anyone looking to leverage ML to solve business problems at scale. Andriy has done a great job in breaking down tasks needed to move a model to production. It is a perfect follow up to his first book- Hundred Page ML which is a great read as well.

Reviewed in the United States on June 18, 2022

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It is a great source you can use right before interview

One person found this helpful

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Bryan Freeman

The best machine learning engineering book I have read

Reviewed in the United States on May 26, 2021

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If you want a deeper understanding of the emerging fields of machine learning engineering and MLOps, this is the book to start with. The author covers the topics very well, from end to end. Highly recommend!

One person found this helpful

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Raphael

Didático e completo

Reviewed in Brazil on January 8, 2023

Verified Purchase

Livro com explicações objetivas e práticas de problemas reais.

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Amazon buyer

Machine learning

Reviewed in India on February 22, 2024

Verified Purchase

Good book

JC

Book looks great, it must have been compiled with LaTeX

Reviewed in Canada on October 19, 2020

Verified Purchase

Great book amazing format

Spyros Chouliaras

Great Overview of Machine Learning Engineering

Reviewed in the United Kingdom on July 15, 2021

Verified Purchase

The book defines the machine learning project life cycle and presents theory and strategies behind each step. Furthermore, the author presents code snippets to demonstrate the key ideas but do not expect any coding solution similar to "Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems" and "Approaching (Almost) Any Machine Learning Problem" books. Similar to "The Hundred-Page Machine Learning Book" the quality of the book is great.

AGain

Interessante Metodologia per progetti di ML

Reviewed in Italy on June 7, 2021

Verified Purchase

l'idea di scrivere un libro sulla metodologia da seguire laddove si voglia affrontare un progetto centrato sul machine learning è interessante in quanto distoglie dai dettagli tecnici dei singoli aspetti e aiuta a vedere il problema nel suo insieme. chiaramente per ogni fase l'autore descrive a grandi linee gli aspetti tecnici coinvolti sebbene per l'approfondimento di questo sia necessario andare su altri testi o su internet per capire di cosa si tratta. ciò nonostante ritengo il libro ben fatto e utile come riferimento per l'impostazione di progetto di questo tipo.

2 people found this helpful

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