[2410.03795] Deep Learning and Machine Learning: Advancing Big Data Analytics and Management with Design Patterns
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Computer Science > Software Engineering

arXiv:2410.03795 (cs)
[Submitted on 4 Oct 2024 (v1), last revised 6 Dec 2024 (this version, v2)]

Title:Deep Learning and Machine Learning: Advancing Big Data Analytics and Management with Design Patterns

Authors:Keyu Chen, Ziqian Bi, Tianyang Wang, Yizhu Wen, Pohsun Feng, Qian Niu, Junyu Liu, Benji Peng, Sen Zhang, Ming Li, Xuanhe Pan, Jiawei Xu, Jinlang Wang, Ming Liu
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Abstract:This book, Design Patterns in Machine Learning and Deep Learning: Advancing Big Data Analytics Management, presents a comprehensive study of essential design patterns tailored for large-scale machine learning and deep learning applications. The book explores the application of classical software engineering patterns, Creational, Structural, Behavioral, and Concurrency Patterns, to optimize the development, maintenance, and scalability of big data analytics systems. Through practical examples and detailed Python implementations, it bridges the gap between traditional object-oriented design patterns and the unique demands of modern data analytics environments. Key design patterns such as Singleton, Factory, Observer, and Strategy are analyzed for their impact on model management, deployment strategies, and team collaboration, providing invaluable insights into the engineering of efficient, reusable, and flexible systems. This volume is an essential resource for developers, researchers, and engineers aiming to enhance their technical expertise in both machine learning and software design.
Comments: 138pages
Subjects: Software Engineering (cs.SE); Machine Learning (cs.LG)
Cite as: arXiv:2410.03795 [cs.SE]
  (or arXiv:2410.03795v2 [cs.SE] for this version)
  https://doi.org/10.48550/arXiv.2410.03795

Submission history

From: Ming Liu [view email]
[v1] Fri, 4 Oct 2024 02:50:58 UTC (80 KB)
[v2] Fri, 6 Dec 2024 06:59:09 UTC (79 KB)
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