MACHINE LEARNING AND AI APPLICATIONS IN EDGE COMPUTING

In recent years, the convergence of Artificial Intelligence (AI) and Machine Learning (ML) with Edge Computing has transformed industries worldwide. By moving intelligence closer to data
sources, this integration enables faster insights, real-time decision-making, and enhanced efficiency. Traditionally, cloud computing handled data processing in
centralized centers. However, with the rapid growth of IoT devices and the need for low-latency operations, Edge Computing emerged to process data locally—reducing delays, improving
reliability, and minimizing dependence on remote servers. Combining AI with Edge Computing has opened new possibilities in healthcare, transportation, industrial automation, smart cities, and retail. This synergy supports instant analytics, autonomous systems, and intelligent operations at the network edge.
This book, Artificial Intelligence and Machine Learning for Edge Computing, serves as a comprehensive guide for researchers, engineers, professionals, and students. It covers both fundamentals and advanced concepts, including AI system architecture, development tools, 5G, Edge Analytics, Fog Computing, and Microservices Architecture.
Book Structure:
 Part 1: Fundamentals, tools, and applications of AI and Edge Computing
 Part 2: Microservices Architecture for edge environments
 Part 3: Edge Data Analytics and real-world use cases
 Part 4: Challenges and advancements—security, privacy, scalability, performance
 Part 5: Future of Edge AI with 5G, IoT, and deep learning
 Part 6: Integration of ML with Edge Computing for adaptive intelligence

The fusion of AI and Edge Computing represents a global technological transformation, driving innovation, operational efficiency, and digital growth. The book acknowledges the contributions of researchers and industry experts whose insights and efforts have guided the development of this work.