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

AI thought leader and ex Google China CEO said “ we have now moved from the age of AI discovery (focused on research) to one of AI implementation that is all about the application of the technology to real-world problems”.

Andriy Burkov‘s MLE book is a guide on how to actually do that!

Written in an easy to understand fashion and at just the right level of detail this is one of the books that will look wrecked swiftly in your workspace because you use it so much to look things up.

Thank you Andriy!

Machine Learning Engineering already starts with an amazing foreword Cassie Kozyrkov, which nicely introduced the problem between ML-Research and ML-Solution. The author Andriy Burkov really highlights the needs and challenges to establish independent ML solutions. Especially in a business context, reading this book allows you to design more realistic business and project cases.

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.

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.

Great book amazing format

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