Building Scalable Edge AI Solutions for the Internet of Things

Building Scalable Edge AI Solutions for the Internet of Things" explores the convergence of IoT, edge computing, and AI, providing a practical guide to developing and deploying intelligent edge solutions. The book begins by establishing foundational concepts, explaining the limitations of cloud-centric IoT architectures and introducing the benefits of edge computing and Edge AI, such as reduced latency, bandwidth efficiency, and enhanced privacy. It then delves into the building blocks of Edge AI solutions, covering data acquisition and preprocessing techniques optimized for resource-constrained devices, model selection and compression strategies, and an overview of relevant frameworks and hardware platforms like MCUs, MPUs, GPUs, and FPGAs.

The book further provides a practical development approach, detailing the steps from problem definition and data preparation to model training, evaluation, and deployment on edge devices. It emphasizes the importance of robust deployment strategies, including OTA updates, device management tools, and crucial security considerations. Finally, the book examines advanced topics like real-time Edge AI applications in industrial automation, robotics, and autonomous systems, along with scalability and orchestration strategies for large deployments. It concludes by exploring future trends, including emerging hardware and software, the role of 5G, and the ethical implications of Edge AI, advocating for responsible AI development.