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Scalable and Distributed Machine Learning and Deep Learning
Patterns is a practical guide that provides insights into how
distributed machine learning can speed up the training and serving
of machine learning models, reduce time and costs, and address
bottlenecks in the system during concurrent model training and
inference. 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. The book is suitable for
machine learning professionals, researchers, and students who want
to learn about distributed machine learning techniques and apply
them to their work. This book is an essential resource for
advancing knowledge and skills in artificial intelligence, deep
learning, and high-performance computing. The book is suitable for
computer, electronics, and electrical engineering courses focusing
on artificial intelligence, parallel computing, high-performance
computing, machine learning, and its applications. Whether you're a
professional, researcher, or student working on machine and deep
learning applications, this book provides a comprehensive guide for
creating distributed machine learning, including multi-node machine
learning systems, using Python development experience. By the end
of the book, readers will have the knowledge and abilities
necessary to construct and implement a distributed data processing
pipeline for machine learning model inference and training, all
while saving time and costs.
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