Paperback. AI for Edge Computing presents a concise and systematic introduction to the integration of artificial intelligence with edge computing for real-time and efficient data processing near the data source. The book addresses the limitations of cloud-centric AI systems, such as latency, bandwidth usage, and privacy concerns, and explains how edge intelligence enables faster, reliable, and context-aware decision making. It covers the fundamentals of edge computing architectures, edge hardware platforms, and embedded AI systems, including commonly used devices and accelerators. Lightweight machine learning and deep learning models optimized for edge deployment are discussed along with model compression and optimization techniques. The book also introduces deployment strategies, real-time inference, and widely used frameworks, while highlighting security, privacy, and emerging trends. Designed with an academic focus, this book serves as a valuable resource for students and professionals in artificial intelligence, embedded systems, and edge computing. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

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Bibliographic Details

Title: AI for Edge Computing (Paperback)
Publisher: LAP Lambert Academic Publishing
Publication Date: 2026
Language: English
ISBN 10: 6209592953
ISBN 13: 9786209592959
Binding: Paperback
Condition: new

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Synopsis

AI for Edge Computing presents a concise and systematic introduction to the integration of artificial intelligence with edge computing for real-time and efficient data processing near the data source. The book addresses the limitations of cloud-centric AI systems, such as latency, bandwidth usage, and privacy concerns, and explains how edge intelligence enables faster, reliable, and context-aware decision making. It covers the fundamentals of edge computing architectures, edge hardware platforms, and embedded AI systems, including commonly used devices and accelerators. Lightweight machine learning and deep learning models optimized for edge deployment are discussed along with model compression and optimization techniques. The book also introduces deployment strategies, real-time inference, and widely used frameworks, while highlighting security, privacy, and emerging trends. Designed with an academic focus, this book serves as a valuable resource for students and professionals in artificial intelligence, embedded systems, and edge computing.

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