Overview

Mastering Edge AI and Real-Time Analytics - Volume II Deployment, Distributed Intelligence, and Real-World Edge AI Applications As Edge AI technologies mature, the focus shifts from foundational architecture to the practical challenges of deploying and managing intelligent systems at scale. Modern organizations are integrating edge intelligence into industrial automation, smart cities, healthcare monitoring platforms, robotics systems, and autonomous technologies. These environments require reliable deployment strategies, distributed computing frameworks, and continuous monitoring of AI-powered systems. Mastering Edge AI and Real-Time Analytics - Volume II focuses on the operational side of Edge AI. This volume explores how optimized AI models are deployed, orchestrated, and maintained across distributed networks of devices operating in real-world environments. Readers will gain insights into deployment frameworks such as TensorFlow Lite, ONNX Runtime, and PyTorch Mobile, along with modern infrastructure technologies including containerization, edge orchestration platforms, and distributed system management. The book also explores advanced topics such as federated learning, collaborative inference, device fleet management, and performance monitoring across edge networks. Beyond infrastructure and deployment, this volume examines real-world applications of Edge AI across multiple industries. Detailed discussions highlight how intelligent edge systems are transforming industrial IoT, predictive maintenance, healthcare monitoring, autonomous robotics, and smart city infrastructure. Mastering Edge AI and Real-Time Analytics - Volume II equips engineers, researchers, and technology professionals with the knowledge required to deploy, scale, and manage intelligent edge systems in modern distributed environments.

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