Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications

Foreword

This pragmatic book introduces both machine learning and data science, bridging gaps between data scientist and engineer, and helping you bring these techniques into production. It helps ensure that your efforts actually solve your problem, and offers unique coverage of real-world optimization in production settings. This book is filled with code examples in Python and visualizations to illustrate concepts in algorithms. Validation, hypothesis testing, and visualization are introduced early on as these are all key to ensuring that your efforts in data science are actually solving your problem. Part III of the book is unique among data science and machine learning books because of its focus on real-world concerns in optimization. Thinking ...