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  • Practical MLOps: Operationalizing Machine Learning Models

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Practical MLOps: Operationalizing Machine Learning Models 1st Edition

by Noah Gift (Author), Alfredo Deza (Author)
3.9 out of 5 stars (58)
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Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models.

Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. This book gives you a head start.

You'll discover how to:

  • Apply DevOps best practices to machine learning
  • Build production machine learning systems and maintain them
  • Monitor, instrument, load-test, and operationalize machine learning systems
  • Choose the correct MLOps tools for a given machine learning task
  • Run machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware

Read more
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  1. ISBN-10
    1098103017
  2. ISBN-13
    978-1098103019
  3. Edition
    1st
  4. Publisher
    O'Reilly Media
  5. Publication date
    October 19, 2021
  6. Language
    English
  7. Dimensions
    6.75 x 1 x 8.75 inches
  8. Print length
    458 pages
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From the Publisher

Practical MLOps

How This Book Is Organized

We designed this book so that you can consume each chapter as a standalone section designed to give you immediate help. At the end of each chapter are discussion questions that are intended to spur critical thinking and technical exercises to improve your understanding of the material.

These discussion questions and exercises are also well suited for use in the classroom in a Data Science, Computer Science, or MBA program and for the motivated self-learner. The final chapter contains several case studies helpful in building a work portfolio as an expert in MLOps.

The book is divided into 12 chapters, which we’ll break down a little more in the following section. At the end of the book, there is an appendix with a collection of valuable resources for implementing MLOps.

Chapters

The first few chapters cover the theory and practice of both DevOps and MLOps. One of the items covered is how to set up continuous integration and continuous delivery. Another critical topic is Kaizen, i.e., the idea of continuous improvement in everything.

There are three chapters on cloud computing that cover AWS, Azure, and GCP. Alfredo, a developer advocate for Microsoft, is an ideal source of knowledge for MLOps on the Azure platform. Likewise, Noah has spent years getting students trained on cloud computing and working with the education arms of Google, AWS, and Azure. These chapters are an excellent way to get familiar with cloud-based MLOps.

Other chapters cover critical technical areas of MLOps, including AutoML, containers, edge computing, and model portability. These topics encompass many cutting-edge emerging technologies with active traction.

Finally, in the last chapter, Noah covers a real-world case study of his time at a social media startup and the challenges they faced doing MLOps.

Appendixes

The appendixes are a collection of essays, ideas, and valuable items that cropped up in years between finishing Python for DevOps (O’Reilly) and this book. The primary way to use them is to help you make decisions about the future.

Exercise Questions

In this book’s exercises, a helpful heuristic considers how you can leverage them into a portfolio using GitHub and a YouTube walkthrough of what you did. In keeping with the expression “a picture is worth a thousand words,” a YouTube link to a walkthrough of a reproducible GitHub project on a resume may be worth 10,000 words and puts the resume in a new category of qualification for a job.

Editorial Reviews

About the Author

Noah Gift is the founder of Pragmatic A.I. Labs. He lectures at MSDS, at Northwestern, Duke MIDS Graduate Data Science Program, the Graduate Data Science program at UC Berkeley, the UC Davis Graduate School of Management MSBA program, UNC Charlotte Data Science Initiative, and University of Tennessee (as part of the Tennessee Digital Jobs Factory). He teaches and designs graduate machine learning, MLOps, AI, and data science courses, and consulting on machine learning and cloud architecture for students and faculty. As a former CTO, individual contributor, and consultant he has over 20 years' experience shipping revenue-generating products in many industries including film, games, and SaaS.

Alfredo Deza is a passionate software engineer, speaker, author, and former Olympic athlete with almost two decades of DevOps and software engineering experience. He currently teaches Machine Learning Engineering and gives worldwide lectures about software development, personal development, and professional sports. Alfredo has written several books about DevOps and Python, and continues to share his knowledge about resilient infrastructure, testing, and robust development practices in courses, books, and presentations.

Product details

  • Publisher ‏ : ‎ O'Reilly Media
  • Publication date ‏ : ‎ October 19, 2021
  • Edition ‏ : ‎ 1st
  • Language ‏ : ‎ English
  • Print length ‏ : ‎ 458 pages
  • ISBN-10 ‏ : ‎ 1098103017
  • ISBN-13 ‏ : ‎ 978-1098103019
  • Item Weight ‏ : ‎ 2.31 pounds
  • Dimensions ‏ : ‎ 6.75 x 1 x 8.75 inches
  • Best Sellers Rank: #152,074 in Books (See Top 100 in Books)
  • Customer Reviews:
    3.9 out of 5 stars (58)

About the author

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Noah Gift

Noah Gift

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Noah Gift is the founder of Pragmatic AI Labs. Noah Gift lectures at MSDS, at Northwestern, Duke MIDS Graduate Data Science Program, and the Graduate Data Science program at UC Berkeley and the UC Davis Graduate School of Management MSBA program, and UNC Charlotte Data Science Initiative. He is teaching and designing graduate machine learning, A.I., Data Science courses, and consulting on Machine Learning and Cloud Architecture for students and faculty. These responsibilities include leading a multi-cloud certification initiative for students.

Noah is a Python Software Foundation Fellow, and AWS ML Hero. He currently holds the following industry certifications for AWS: AWS Subject Matter Expert (SME) on Machine Learning, AWS Certified Solutions Architect, and AWS Certified Machine Learning Specialist, AWS Certified Big Data Specialist, AWS Academy Accredited Instructor, AWS Faculty Ambassador. He also is certified on both the Google and Azure platform: Google Certified Professional Cloud Architect, Certified Microsoft MTA on Python. He has published over 100 technical publications including multiple books on subjects ranging from Cloud Machine Learning to DevOps. Publications appear in Forbes, IBM, Red Hat, Microsoft, O’Reilly, Pearson, Udacity, Coursera, datascience.com, and DataCamp. Workshops and Talks around the world for organizations including NASA, PayPal, PyCon, Strata, O’Reilly Software Architecture Conference, and FooCamp. As an SME on Machine Learning for AWS, he helped created the AWS Machine Learning certification.

He has worked in roles ranging from CTO, General Manager, Consulting CTO, Consulting Chief Data Scientist, and Cloud Architect. This experience has been with a wide variety of companies: ABC, Caltech, Sony Imageworks, Disney Feature Animation, Weta Digital, AT&T, Turner Studios, and Linden Lab, and industries: Television, Film, Games, SaaS, Sports, Telecommunications. He has film credits in many major motion pictures for technical work, including Avatar, Spider-Man 3, and Superman Returns.

He has been responsible for shipping many new products at multiple companies that generated millions of dollars of revenue and had a global scale. Currently, he is consulting startups and other companies, on Machine Learning, Cloud Architecture, and CTO level consulting as the founder of Pragmatic A.I. Labs.

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Customer reviews

3.9 out of 5 stars
58 global ratings
great mlops book packed with practical experiences, best practices, tools and working codes
5 out of 5 stars
great mlops book packed with practical experiences, best practices, tools and working codes
it not only covers all the nuts and bolts on building machine learning systems in production, but also maintain them Monitor, instrument, and operationalize machine learning systems. particularly like the details on all major cloud platform on AWS, Google Cloud Platform and Microsoft Azure.
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Top reviews from the United States

  • AT
    5.0 out of 5 stars Practical book
    Reviewed in the United States on March 16, 2025
    Format: Paperback
    Really helpful, practical book! As a tech professional, this helped me with my work.
    Helpful
     Report
  • Jay G.
    3.0 out of 5 stars An average book
    Reviewed in the United States on November 4, 2025
    Format: PaperbackVerified Purchase
    It’s an average book that provides both high-level and low-level views, but the consistency is lacking. The book heavily focuses on cloud service provider tools and neglects to mention open-source alternatives. While I found it mildly helpful, I’m not sure who would be a suitable target audience for this book.
    Helpful
     Report
  • Amazon Customer
    5.0 out of 5 stars Great book
    Reviewed in the United States on November 23, 2021
    Format: Paperback
    The book does a great job at covering both theory and practice when dealing with ML Engineering and ML Ops. Having a book like this that focuses on applied machine learning, ML Engineering, and MLOps in the cloud would help equip an ML practitioner with the knowledge needed to contribute to real work projects. It also contains relevant practical tips at the end (Appendix section) which would help a lot of tech professionals in their careers and in their lives. How I wish I had this book years ago!
    4 people found this helpful
    Helpful
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  • Gabriel Freire Luna
    4.0 out of 5 stars Great content, poor quality of printed version
    Reviewed in the United States on May 30, 2022
    Format: PaperbackVerified Purchase
    This book had awesome content, such a shame the quality OReilly decided to go withis so poor. Cannot even highlight on it because it goes through the other side of the page
    4 people found this helpful
    Helpful
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  • Art
    5.0 out of 5 stars One of the best books to deploy to production data science models in AWS
    Reviewed in the United States on November 12, 2021
    Format: Kindle
    the good: one of the best books to move to production data science models in AWS, it has step by step instructions how to develop and deploy models.

    the bad, it's not for beginners, you need to have some knowledge of data science, as well as what cloud services are.
    5 people found this helpful
    Helpful
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  • Chris Blackburn
    1.0 out of 5 stars Hastily bolted together with blog-level writing
    Reviewed in the United States on May 31, 2022
    Format: PaperbackVerified Purchase
    This book is not great. Overall, it feels hastily thrown together, as if a deadline was rapidly approaching. The visualizations are poorly thought out and lack imagination, particularly the ones meant to illustrate a concept. A lot of the writing is something you would expect to see in blog post, e.g. repetition, surface level insights, and shallow applications. Echoing another user, I am not sure this book meets O’Reilly’s standards.
    16 people found this helpful
    Helpful
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  • bill
    5.0 out of 5 stars great mlops book packed with practical experiences, best practices, tools and working codes
    Reviewed in the United States on January 14, 2022
    Format: Paperback
    it not only covers all the nuts and bolts on building machine learning systems in production, but also maintain them Monitor, instrument, and operationalize machine learning systems. particularly like the details on all major cloud platform on AWS, Google Cloud Platform and Microsoft Azure.
    Customer image
    bill
    5.0 out of 5 stars
    great mlops book packed with practical experiences, best practices, tools and working codes

    Reviewed in the United States on January 14, 2022
    it not only covers all the nuts and bolts on building machine learning systems in production, but also maintain them Monitor, instrument, and operationalize machine learning systems. particularly like the details on all major cloud platform on AWS, Google Cloud Platform and Microsoft Azure.
    Images in this review
    Customer image
    3 people found this helpful
    Helpful
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  • AP
    2.0 out of 5 stars Does not follow a logical order and is not very helpful for beginners
    Reviewed in the United States on April 13, 2025
    Format: Paperback
    Weirdly written
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Top reviews from other countries

  • the_bearded_reviewer
    1.0 out of 5 stars Worst book from O’Reilly i‘ve ever ‚read‘
    Reviewed in Germany on May 10, 2022
    Format: PaperbackVerified Purchase
    Unfortunately this book doesn’t hold up to the high standards from O’Reilly. The layout and print quality is a disaster for the price, many visuals in the book are at the level of a 5th grader. Overall, It’s feels much more like a self published book.

    On a positive note: the author provides his views on dieting and his home office set up… … yes, really…so if your interested in that kind of knowledge, this book might be useful to you.
    Report
  • Youssefmrini
    5.0 out of 5 stars Awesome book
    Reviewed in France on January 5, 2022
    Format: PaperbackVerified Purchase
    I really like it. The content is awesome
    Report
  • laksh
    5.0 out of 5 stars Very informative
    Reviewed in India on February 10, 2022
    Format: KindleVerified Purchase
    ML ops made easy.
    I strongly recommend this for all Python programmer who wish to become Data scientist or ML engineer. It is a shelvable.
    Report
  • Sujan Sivaji
    5.0 out of 5 stars Comprehensive MLOPS explanation
    Reviewed in India on September 11, 2022
    Format: KindleVerified Purchase
    Important concepts summarised.
    Report
  • Darko D.
    2.0 out of 5 stars Very bad quality and full of irrelevant filler text.
    Reviewed in Germany on July 4, 2025
    Format: PaperbackVerified Purchase
    1. What is this book about? Biography of the author and or MLOps?
    2. The graphics are amateurish grade and pale in print, and the editing is just hilarious, as the attached example shows where they have reacted the image and they couldn’t even care to use line vs freehand.
    3. The book rambles about arbitrary and trivial topics such as “when did the author hear about packaging ML models for the first time” and when is it that the reader might have had.
    Customer image
    Darko D.
    2.0 out of 5 stars
    Very bad quality and full of irrelevant filler text.

    Reviewed in Germany on July 4, 2025
    1. What is this book about? Biography of the author and or MLOps?
    2. The graphics are amateurish grade and pale in print, and the editing is just hilarious, as the attached example shows where they have reacted the image and they couldn’t even care to use line vs freehand.
    3. The book rambles about arbitrary and trivial topics such as “when did the author hear about packaging ML models for the first time” and when is it that the reader might have had.
    Images in this review
    Customer image Customer image Customer image Customer image
    Report
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