Machine Learning at the Edge: Revolutionizing Intelligent Computing Beyond the Cloud
Machine Learning at the Edge: Revolutionizing Intelligent Computing Beyond the Cloud

Author(s): Lyuben Kirev
Subject(s): Economy, Business Economy / Management, ICT Information and Communications Technologies, Business Ethics, Green Transformation
Published by: Икономически университет - Варна
Keywords: machine learning at the edge; cloud computing; edge systems; optimization; efficiency
Summary/Abstract: Machine learning at the edge (Edge ML) represents a paradigm shift from centralized cloud computing to decentralized, resource-constrained devices. This article examines the technical underpinnings, economic implications, and transformative potential of deploying ML models directly on edge devices (e.g., sensors, smartphones, IoT hardware). Driven by critical requirements for low-latency decision-making (e.g., autonomous vehicles), data privacy (e.g., healthcare), reduced operational costs (e.g., bandwidth savings), and offline functionality, Edge ML enables intelligent applications in IoT, smart cities, and industrial automation. While challenges in hardware limitations, model optimization, and security persist, advances in TinyML, federated learning, and specialized accelerators are rapidly evolving the ecosystem. Economically, Edge ML reduces cloud dependency, enables new business models, and optimizes total cost of ownership (TCO)—despite higher initial hardware investments. This article argues that Edge ML is indispensable for scalable, privacy-compliant, and economically viable intelligent systems.

• Details
• Contents