BUILDING RESILIENT AND ADAPTIVE AI MODELS FOR REAL-TIME ANALYTICS IN CLOUD ENVIRONMENTS

Abstract

This article presents an innovative framework for developing resilient and adaptive artificial intelligence (AI) models specifically designed for real-time analytics in cloud computing environments. The proposed approach introduces a novel Dynamic Learning Rate Adaptation (DLRA) mechanism combined with Cloud-Native Resilience Patterns (CNRP) to create self-adjusting models that maintain high performance under varying workloads. The article implements an Adaptive Performance Enhancement Protocol (APEP) for resource optimization and an Integrity-Preserving Stream Processing (IPSP) mechanism for data consistency. The architecture utilizes microservices with containerization technologies, incorporating a Multi-Stage Recovery Protocol (MSRP) and Dynamic Resource Allocator (DRA) for enhanced system resilience. Experimental results demonstrate significant improvements, including a reduction in response time, sustained model accuracy, and reduction in resource utilization compared to traditional approaches. The system maintains performance under high loads and achieves availability during peak usage. The framework also shows remarkable adaptation capabilities with learning rate convergence within 5 iterations and resource allocation optimization in less than 1 second. These results validate the effectiveness of the approach in addressing the challenges of real-time analytics in cloud environments while providing a foundation for future advancements in autonomous systems and edge computing applications.