Building Intelligent Systems: A Guide to Machine Learning Engineering

This book is mostly about how to think about intelligent system from a very high level.This thinking process is good but I feel like I can do that by myself when needed.On the other hand, it lacks the concrete details over how to exactly design intelligence system under difference circumstances.This is what I originally hoped I could learn from this book but it has nothing on this side.Overall, not recommended, especially for less experienced person like me.

As a Machine Learning scientists working at a large software engineering company, I strongly feel like this book should be one of the mandatory readings for anybody working on real-world machine learning systems, regardless of their role (software engineer, data scientist, product manager, etc.). Most Machine Learning books will teach you the various ways you can train a model, what loss function to use, how to avoid overfitting, etc, but none of them will explain to you the most important part: how to ship your model to have customer impact, and how to design your user experience such that every user interaction improves your sytem. This book explains it in great details, with lots of concrete examples and very clear explanations about each and every steps required to deliver a successful intelligent experience. The summary at the end of each chapter are very useful when coming back to the book later to review a section! I also really liked the tone of the book, full of funny examples and witty remarks. Way to make a technical read entertaining!a

I took a number of Machine Learning courses in school. But none of them taught me what I need to work on Machine Learning in the industry. Having built Machine Learning models and backend services for a few years at Microsoft, I can attest to the quality content of this book. Geoff did a great job of breaking down the real and practical process to build Machine Learning services. This book would serve perfectly as a good read for people getting into Machine Learning Engineering, as well as a handbook for experienced engineers who need to consult from time to time.

Building machine learning models that work is hard in and of itself, but actually creating a product out of them and shipping them requires thinking about building software in an entirely different way.I was really happy to find this book that helps bridge the gap between building a model and shipping an intelligent product. There's a lot of tricky nuance there and getting it right is hard. You can tell that Hulten has shipped enough successful machine learning products that he's figured out a lot this through trial and error and by applying what I'm sure is tons of real world engineering experience.Finally a book that teaches us how to be successful and building ML based products!Also, as a bonus, while this is a technical book, it's actually very well written in clear concise language. This is a welcome change from the slew of poorly written technical manuals that seem to clog the ML space.If you're anywhere close to the ML space: READ THIS BOOK!

I’m going to be joining a workgroup at my job related to incorporating intelligent systems. Prior to reading this book, I had little more than a layman’s knowledge of how these systems work but now I feel prepared to have intelligent conversations with the programmers.The book isn’t merely an introduction to intelligent systems. It also provides a meaningful analysis of options available and their pros and cons as well as various pitfalls along the path of production, implementation, and maintenance.The book is excellently organized with summaries and synopses which will make it easy for me to go back and quickly find sections I need to review again as they become appropriate.