Machine Learning Logistics

Preface

Machine learning offers a rich potential for expanding the way we work with data and the value we can mine from it. To do this well in serious production settings, it’s essential to be able to manage the overall flow of data and work, not only in a single project, but also across organizations.

This book is for anyone who wants to know more about getting machine learning model management right in the real world, including data scientists, architects, developers, operations teams, and project managers. Topics we discuss and the solutions we propose should be helpful for readers who are highly experienced with machine learning or deep learning as well as for novices. You don’t need a background in statistics or mathematics to take advantage of most of the content, with the exception of evaluation and metrics analysis.

How This Book is Organized

Chapters 1 and 2 provide a fundamental view of why model management matters, what is involved in the logistics and what issues should be considered in designing and implementing an effective project.

Chapters 3 through 7 provide a solution for the challenges of data and model management. We describe in detail a preferred architecture, the rendezvous architecture, that addresses the needs for working with multiple models, for evaluating and comparing models effectively, and for being able to deploy to production with a seamless hand-off into a predictable environment.

Chapter 8 draws final lessons. In Appendix A, we offer a list of ...