Scalable AI Solutions for Edge-Cloud Collaborative Computing: Challenges and Innovations

Abstract

The rapid growth of Internet of Things (IoT) devices and the increasing complexity of data-driven applications have necessitated the development of scalable AI solutions that seamlessly integrate edge and cloud computing. This paper explores the challenges and innovations associated with implementing scalable AI solutions for edge-cloud collaborative computing. We address key issues such as latency, bandwidth constraints, and resource management, and propose novel AIdriven strategies to overcome these challenges. Our approach emphasizes the synergistic benefits of combining edge and cloud resources to enhance computational efficiency, reduce latency, and optimize data processing. We present case studies and experimental results to demonstrate the effectiveness of these solutions in real-world scenarios, offering insights into the future of scalable AI in edge-cloud environments.