Scalable and Distributed Machine Learning and Deep Learning Patterns, edited by J. Joshua Thomas, Harini Sriraman, and Pattabiraman Venkatasubbu, is a comprehensive guide for machine learning professionals, researchers, and students who want to learn about distributed machine learning techniques. The editors provide a practical approach to creating distributed machine learning, including multi-node machine learning systems, using Python development experience.The book covers various topics related to distributed machine learning such as data parallelism, model parallelism, and hybrid parallelism. Readers will learn about cutting-edge parallel techniques for serving and training models such as parameter server and all-reduce, pipeline input, intra-layer model parallelism, and a hybrid of data and model parallelism. Additionally, the book covers the advantages and disadvantages of pipeline parallelism, global batch size, learning rate adjustment, and model synchronization schemes.This essential resource is for anyone interested in advancing their knowledge and skills in artificial intelligence, deep learning, and high-performance computing, and is suitable for computer, electronics, and electrical engineering courses focusing on artificial intelligence, parallel computing, high-performance computing, machine learning, and its applications.This must-read reference book will empower anyone looking to understand and apply the latest distributed machine learning techniques to their work. The book provides practical examples and is well-organized, making it easy to follow along and apply the concepts covered.

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