Overview
- Presents a comprehensive overview of distributed machine learning
- Introduces the progress of gradient optimization for distributed machine learning
- Addresses the key challenge of implementing machine learning in the context of big data and large-scale systems
Part of the book series: Big Data Management (BIGDM)
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Table of contents (5 chapters)
Keywords
About this book
This book presents the state of the art in distributed machine learning algorithms that are based on gradient optimization methods. In the big data era, large-scale datasets pose enormous challenges for the existing machine learning systems. As such, implementing machine learning algorithms in a distributed environment has become a key technology, and recent research has shown gradient-based iterative optimization to be an effective solution. Focusing on methods that can speed up large-scale gradient optimization through both algorithm optimizations and careful system implementations, the book introduces three essential techniques in designing a gradient optimization algorithm to train a distributed machine learning model: parallel strategy, data compression and synchronization protocol.
Written in a tutorial style, it covers a range of topics, from fundamental knowledge to a number of carefully designed algorithms and systems of distributed machine learning. It will appealto a broad audience in the field of machine learning, artificial intelligence, big data and database management.
Authors and Affiliations
About the authors
Jiawei Jiang obtained his PhD from Peking University 2018, advised by Prof. Bin Cui. His research interests include distributed machine learning, gradient optimization and automatic machine learning. He has served as a program committee member or reviewer for various international events, including SIGMOD, VLDB, ICDE, KDD, AAAI and TKDE. He was awarded the CCF Outstanding Doctoral Dissertation Award (2019) and ACM China Doctoral Dissertation Award (2018).
Bin Cui is a Professor at the School of EECS and Director of the Institute of Network Computing and Information Systems, at Peking University. His research interests include database system architectures, query and index techniques, and big data management and mining. He has published over 200 refereed papers at international conferences and in journals. Dr. Cui has served on the technical program committee of various international conferences, including SIGMOD, VLDB, ICDE and KDD, and as Vice PC Chair of ICDE 2011, Demo Co-Chair of ICDE 2014, Area Chair of VLDB 2014, PC Co-Chair of APWeb 2015 and WAIM 2016. He is currently a member of the trustee board of VLDB Endowment, is on the editorial board of the VLDB Journal, Distributed and Parallel Databases Journal, and Information Systems, and was formerly an associate editor of IEEE Transactions on Knowledge and Data Engineering (TKDE, 2009-2013). He was selected for a Microsoft Young Professorship award (MSRA 2008), CCF Young Scientist award (2009), Second Prize of Natural Science Award of MOE China (2014), and appointed a Cheung Kong distinguished Professor by the MOE in 2016.
Bibliographic Information
Book Title: Distributed Machine Learning and Gradient Optimization
Authors: Jiawei Jiang, Bin Cui, Ce Zhang
Series Title: Big Data Management
DOI: https://doi.org/10.1007/978-981-16-3420-8
Publisher: Springer Singapore
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022
Hardcover ISBN: 978-981-16-3419-2Published: 24 February 2022
Softcover ISBN: 978-981-16-3422-2Published: 25 February 2023
eBook ISBN: 978-981-16-3420-8Published: 23 February 2022
Series ISSN: 2522-0179
Series E-ISSN: 2522-0187
Edition Number: 1
Number of Pages: XI, 169
Number of Illustrations: 1 b/w illustrations
Topics: Machine Learning, Data Mining and Knowledge Discovery, Database Management