Machine Learning Engineering

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.

Great book amazing format