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

This book has been a genuine pleasure to read.There are many many books out there on Machine Learning detailing techniques, architectures, and frameworks but surprisingly this is the first of its kind to address common design patterns. Good ML design patterns hold their relevance over time much more than a framework or architecture might, so it's surprising that this book stands alone in this topic.The design patterns detailed in the book showcase the experience of the authors and clearly the patterns have emerged from the trenches of production to prove themselves battle tested! The authors understand that like most things in Software Engineering it's all about tradeoffs when making decisions around machine learning problems. Every pattern in the book is clearly framed, laid out, and explained.I'd highly recommend this book to any ML practitioner but especially those whose focus is on devoting production ready Ml systems.

The content is presented in a clear and structured manner, making complex concepts understandable. The authors use a step-by-step approach and provide diagrams and code examples that enhance comprehension, making it easy to grasp and apply the design patterns. Book Review: "Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps"This book is a comprehensive guide for anyone navigating the complexities of machine learning. It's a treasure trove of practical solutions and insights that are invaluable for both beginners and seasoned professionals alike. Highly recommended!

I’m half way through the book and I already find it extremely useful. It is very concise and well structured.

Era un regalo para mi novio, lo tuve que devolver porque se lo regalaron repetido!Aun asi, llego en muy buenas condiciones y buen tiempo de entrega, gracias!

It is a good book if you are using Google Cloud AI.It covers many important design patterns for MLE and MLOps that you cannot find in other books.This would be a great book if you are planning to use Google products.Cover should have specified that it is written for Google Cloud AI.I returned it as I prefer to use open-source for this ever-changing field.

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.

This book was inspirational. It is very well structured and provides clear explanation on when a pattern is useful and the alternative you have as an ML practitioner. The book is biased more towards Google Cloud offering and Tenserflow. They sometimes offer alternatives on AWS/Azure and PyTorch -- but not very often.

I am still halfway through the book, but I already find it useful. Many things are usually common knowledge to anyone who has been in the field for a while, but still every section I encounter a good tip. Some reviews say it’s Google/TensorFlow focused, but for me despite not using GCP, this wasn’t a problem still. All tips here can be generalised to other frameworks easily