Optimizing Cloud Infrastructure for Real-time AI Processing: Challenges and Solutions

Abstract

This research paper explores the optimization of cloud infrastructure for real-time artificial intelligence (AI) processing, addressing challenges, solutions, and implications from various perspectives. It discusses scalability issues, latency concerns, resource allocation, security considerations, and cost optimization challenges faced by organizations deploying AI workloads in the cloud. Case studies from diverse industries showcase the tangible benefits of implementing scalable architectures, edge computing integration, specialized hardware utilization, containerization, and data caching techniques. The paper also examines ethical and societal implications, including data privacy, bias, accountability, job displacement, and access disparities. An international perspective highlights regional variations in infrastructure availability, regulatory differences, cultural attitudes, collaboration efforts, and economic impacts. The discussion emphasizes the importance of addressing these challenges while harnessing the economic development opportunities offered by cloud infrastructure optimization for real-time AI processing.