This book attempts to aggregate state-of-the-art research in parallel and distributed machine learning. We believe that parallelization provides a key pathway for scaling up machine learning to large datasets and complex methods. Although large-scale machine learning has been increasingly popular in both industrial and academic research communities, there has been no singular resource covering the variety of approaches recently proposed. We did our best to assemble the most representative contemporary studies in one volume. While each contributed chapter concentrates on a distinct approach and problem, together with their references they provide a comprehensive view of the field.

We believe that the book will be useful to the broad audience of researchers, practitioners, and anyone who wants to grasp the future of machine learning. To smooth the ramp-up for beginners, the first five chapters provide introductory material on machine learning algorithms and parallel computing platforms. Although the book gets deeply technical in some parts, the reader is assumed to have only basic prior knowledge of machine learning and parallel/distributed computing, along with college-level mathematical maturity. We hope that an engineering undergraduate who is familiar with the notion of a classifier and had some exposure to threads, MPI, or MapReduce will be able to understand the majority of the book's content. We also hope that a seasoned expert will find this book full of new, interesting ideas to inspire future research in the area.