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

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