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 mode...

Ausführliche Beschreibung

Bibliographische Detailangaben

Weitere Verfasser:	Thomas, J. Joshua 1973- (HerausgeberIn), Sriraman, Harini 1982- (HerausgeberIn), Venkatasubbu, Pattabiraman 1976- (HerausgeberIn)
Format:	Elektronisch E-Book
Sprache:	English
Veröffentlicht:	Hershey PA, USA IGI Global [2023]
Schriftenreihe:	Advances in computational intelligence and robotics (ACIR) book series
Schlagworte:	Machine learning Deep learning (Machine learning) Algorithms Deep learning Maschinelles Lernen
Online-Zugang:	DE-188 DE-83 DE-91 DE-706 DE-898 DE-1050 Volltext
Zusammenfassung:	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.
Beschreibung:	1 Online-Ressource (xxix, 268 Seiten) Illustrationen, Diagramme
ISBN:	9781668498057
DOI:	10.4018/978-1-6684-9804-0