Machine Learning and Edge Computing for Real-Time Engineering Solutions

Abstract

The fusion of machine learning and edge computing is revolutionizing the design and execution of real-time engineering systems. By enabling data processing and intelligent decision-making at the edge of networks close to where data is generated this paradigm addresses key limitations of traditional centralized architectures, such as latency, bandwidth constraints, and vulnerability to connectivity disruptions. This paper explores the foundational technologies, system architectures, and practical applications of edge-based machine learning in engineering domains, including manufacturing, energy systems, robotics, and infrastructure monitoring. It further examines deployment strategies, security considerations, and reliability mechanisms essential for sustaining high-performance, real-time operations in distributed environments. Together, these insights provide a comprehensive view of how intelligent edge systems can deliver more responsive, efficient, and resilient engineering solutions in the modern era.