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Book Information
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

Book ID Asin: 1098103017
Book Title: Practical MLOps: Operationalizing Machine Learning Models
Book Author: Noah Gift,Alfredo Deza
Book Format and Price:
Book Format Name: Kindle
Book Format Price: $55.09
Book Format Name: Paperback
Book Format Price: $57.99
Book Price: $57.99
Book Category: Books, Computers & Technology, Computer Science and unknown
Book Rating: 44 ratings
Book Product Detail
Publisher ‏ : ‎ O'Reilly Media; 1st edition (October 19, 2021)
Language ‏ : ‎ English
Paperback ‏ : ‎ 458 pages
ISBN-10 ‏ : ‎ 1098103017
ISBN-13 ‏ : ‎ 978-1098103019
Item Weight ‏ : ‎ 1.6 pounds
Dimensions ‏ : ‎ 6.75 x 1 x 8.75 inches
Best Sellers Rank: #422,141 in Books (See Top 100 in Books)
#75 in Machine Theory (Books)
#105 in Business Intelligence Tools
#676 in Artificial Intelligence & Semantics
Customer Reviews:
4.0 4.0 out of 5 stars 44 ratings
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.
Book Review
Name: Gabriel Freire Luna
Rating: 4.0 out of 5 stars
Title: .0 out of 5 stars Great content, poor quality of printed version
Date: Reviewed in the United States on May 30, 2022
Review: 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

Name: Amazon Customer
Rating: 5.0 out of 5 stars
Title: .0 out of 5 stars Great book
Date: Reviewed in the United States on November 23, 2021
Review: 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!

Name: Chris Blackburn
Rating: 1.0 out of 5 stars
Title: .0 out of 5 stars Hastily bolted together with blog-level writing
Date: Reviewed in the United States on May 31, 2022
Review: 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.

Name: bill
Rating: 5.0 out of 5 stars
Title: .0 out of 5 stars great mlops book packed with practical experiences, best practices, tools and working codes
Date: Reviewed in the United States on January 14, 2022
Review: 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.

Name: Art
Rating: 5.0 out of 5 stars
Title: .0 out of 5 stars One of the best books to deploy to production data science models in AWS
Date: Reviewed in the United States on November 12, 2021
Review: 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.

Name: Liss
Rating: 5.0 out of 5 stars
Title: .0 out of 5 stars Solid Book
Date: Reviewed in the United States on November 12, 2021
Review: The authors do a great job weaving personal stories in with practical advice. If you want a book with a little personality along with your best practices, I highly recommend giving this a whirl.

Name: db
Rating: 1.0 out of 5 stars
Title: .0 out of 5 stars He is long winded and misguided
Date: Reviewed in the United States on June 4, 2023
Review: Spends way too much time on concepts that are not relevant. His linkdin learning courses are the same way unfortunately

Name: erika
Rating: 1.0 out of 5 stars
Title: .0 out of 5 stars Not worth the time or money
Date: Reviewed in the United States on June 3, 2022
Review: This book is largely an index of commonly used cloud tools that fails to dig in to how one would actually go about architecting a solution or how to consider the trade-offs between the tools available. The structure of the book also feels very
disorganized, making it hard to get coherent take-aways. Some of the code examples seem to be copied from sources like online tutorials from AWS with minimal modifications. The code that seems to be written by the authors contains poor design decisions,
such as registering models where the only tracking of the code or data versions used to generate the artifact are in a notebook pushed to GitHub. This connection between model, data, and code is central to MLOps and it was surprising to see this in a
book claiming to be about MLOps. The authors also spend many, many pages throughout the book bragging about themselves and self-promoting, which got very tedious and annoying extremely quickly.

Don't waste your time reading this book like I did. Much better options are Machine Learning Design Patterns by Lakshmanan et al. and Agile Data Science 2.0 by Jurney.

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