Great book amazing format

What a beautiful companion to the author’s first book, The Hundred-Page Machine Learning Book.

Were someone who has read neither of these two books to ask me which to buy and read first, I would reply that they might consider buying them both and reading them both at the same time. Burkov’s “read-first, buy-later” policy makes that an easy decision, since you very well may wish to follow that path. Once you are ready to commit the time and if you do not wish to read them both at the same time, which you read first depends on whether you want an overview of ML first, and then an overview of ML engineering, or whether you want a wide vision of end-to-end ML engineering, and only need to get into the details of each ML concept on an as needed basis.

Machine Learning Engineering is about scale. Burkov’s genius is to talk about the end-to-end concerns that those in ML must wrestle with constantly: from model to working pipeline. So that it may scale.

It’s not enough to simply hammer out a model in R or Python, or whatever the language may be and then pass it on. End-to-end ML engineering is a bumpy ride. There are going to be a lot of passengers along for that ride.

Burkov’s ability to summarize something to its essence and still get the message clearly across is exemplified in section 2.2.1: “There are several major unknowns that are almost impossible to guess with confidence unless you worked on a similar project in the past or read about such a project.” Consider that for a moment and recall it to mind the next time you find yourself seated at a business meeting and someone asks: “When do you think we can have the system up and running so we can go forward facing?” Consider the list of unknowns the author lists on pages 23-24.

This book will help you answer that question with some degree of confidence and precision, even if you decide that the best answer at that point in time is: “We don’t know yet. Let’s keep talking about this.”

You will find more than just wisdom in this book. The author gives more than enough specific details on everything from over and under sampling, to the realization that not every project is a machine learning project.

A must have for serious ML practitioners at all levels.

The best book ever for Machine Learning Engineering. This book is different from the ones available in the market which keeps on explaining the algorithms. This book is about the entire procedure and in-depth analysis of the process of Machine Learning and it's steps associated with other technologies. This book probably gives the most simple explanation about the various terms in Data Science and the reasons why are things done the way it is. In one word, this is an exceptional book. I proudly own this book now.

Great book it covers an important gap in the literature: the lifecycle of a ML project. It focuses on the important parts of practical ML, data gathering and preparation, feature engineering, reproducibility, model serving, monitoring, versioning. The book is nicely written. The only caveat is that the book is way to theoretical and is missing practical examples about real projects.

Great book that covers the whole ML lifecycle with interesting considerations and watch outs.

The book defines the machine learning project life cycle and presents theory and strategies behind each step. Furthermore, the author presents code snippets to demonstrate the key ideas but do not expect any coding solution similar to "Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems" and "Approaching (Almost) Any Machine Learning Problem" books. Similar to "The Hundred-Page Machine Learning Book" the quality of the book is great.

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