Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps 1st Edition

My background: I'm an expert software engineer (C++, Java, etc) and proud n00b at machine learning. I've read the O'Reilly "AI and Machine Learning for Coders" book and many online articles. I have a background in trading/financial software, which exposed me to many statistical terms in this book. In the past, PhD level physics/math quants would typically handle those topics and this book has helped me realize some gaps in my knowledge and fill them (sometimes via online search). I can now at least reason about those concepts better even if I don't yet understand the details.I'm 1/3 into the book (so maybe premature for 5 stars) and it's been a dense but interesting read so far. There have been times where I have to lookup terms but the material has still been approachable. The language in the first couple chapters could probably be simplified some but it was sufficient for me with a lot of coffee. I expect to still have very incomplete knowledge after finishing this book due to lack of practical experience. However, my goal is to build a large scaffolding of knowledge/concepts on ML that I can use as a foundation for future learning and broaden my toolbox before I start hacking code. When I was learning C++, I found the Gang of Four book "Design Patterns" accomplished a similar goal to help bridge the gap between academic knowledge and practical software engineering. Much like with the GoF book I suspect I may be re-reading parts of this book in the future when my knowledge has matured. Some may prefer doing a lot of ML coding before reading this book, but I like to have a lot of background knowledge/tools before tackling code - personal preference I guess.I seem to have discovered an error/typo regarding "precision" vs "recall" in chapter 3:Page 135 paragraph 2: "If we care more that our model is correct whenever it makes a positive class prediction we'd optimize our prediction threshold for recall".I think the last word in that sentence should be "precision". The terms are defined on page 124 paragraph 2.

I thought this was a great book for providing people with an understanding of the toolkit that ML engineers need to know when making Machine Learning models.As a side note, I bought this to be better prepared for ML architecture and design interviews.If you are in a hurry, I think the content in Chapters 2, 3, and 4 are great. 5 was somewhat relevant for me and Chapters 6, and 7 are not really relevant until you are actually neck-deep in the models, so they did not really apply to me.Chapter 8 was fantastic since it had a Common Patterns by Use Case and Data Type section, and enumerated many different types of problems and the tools that one might use to tackle them.I am satisfied with what I got from this book.

It is a good book for beginner to build up the knowledge.

This is well written with fabulous examples throughout. It was reassuring to me to see patterns that I use in practice are documented here and there were plenty of inspirational ideas, too.

This book contains a lot of good practices in a easy to read way, so you don't have to digest all the white papers online. I'd love to have the e-book version so I could read some hints while I run the Jupyter Notebooks, but seems that the publisher doesn't allows you to get the e-book with the book so you must buy both.

This is a must-read for scientists and practitioners looking to apply machine learning theory to real life problems. I foresee this book becoming a classical of the discipline’s literature. Very well written and comprehensive description of concepts and applications of design patterns.

The book does a great job in explaining the design pattern with good examples.

The books is mostly from a computer science prospective. I am from an engineering background so my review may be biased. It covers design patters for data treatment, model design to MLOPs. I like the first two sections and my review is based on them. It provides alternative design patterns that I did not know before and they are purely practical. No theories involved