OPTIMIZING CLOUD-BASED AI SOLUTIONS FOR SCALABLE DATA MANAGEMENT AND ANALYTICS

Abstract: This paper discusses cloud-based AI solutions' optimization for scalable data management and analytics. Employing next-generation cloud technologies such as auto-scaling, containerization, and AI integration, we illustrate how scalable data processing can be well-supported by leveraging cloud infrastructure to manage growing data and processing requirements from our research, we found an improvement of 25% in data processing, 40% decrease in latency at high send rates. In addition, cloud optimization of resources led to 36% cost savings on operations, and scalability enabled easy management of up to 450 requests per second with negligible performance impact. Also, AI-driven decision-making tools, combined with cloud-native offerings, demonstrated a 50% increase in predictive accuracy, streamlining business processes and decision-making. The findings show that the integration of AI and cloud computing not only improves scalability and operational effectiveness but also enables cost-effective data management and analytics, providing substantial benefits for organizations moving to cloud-based designs.

Keywords: Cloud Computing, Data Management, Predictive Analytics, Resource Optimization, Data Analytics, Auto-Scaling