AI-Powered Predictive Analytics for Cloud Performance Optimization and Anomaly Detection

Prabhu Chinnasamy

Abstract: This study presents an AI-driven framework for predictive cloud performance monitoring and anomaly detection. Leveraging machine learning models such as PyCaret, LightGBM, and Isolation Forest, the framework enhances system reliability by reducing Mean Time to Resolution (MTTR) by 40%, minimizing false positive alerts by 25%, and detecting anomalies 30 minutes earlier than conventional methods. Unlike static monitoring approaches, this model employs real-time AI-driven insights for intelligent auto-scaling and early failure detection. Validation across finance, healthcare, and retail industries demonstrates a 20% reduction in operational costs and improved resilience during peak workloads. By integrating automated CI/CD pipelines, adaptive model retraining, and AI-powered root cause analysis, this framework offers a self-healing and cost-efficient approach to modern cloud performance monitoring.

Keywords: AI-Driven Performance Monitoring, Proactive Anomaly Detection, Predictive Analytics, Cloud Performance Optimization, Machine Learning in IT Operations, Time-Series Forecasting, PyCaret and XGBoost, Self-Healing Cloud Systems, AI-Powered Root Cause Analysis, CI/CD Pipeline Integration with ML, Generative AI for System Optimization, Multi-Cloud and Hybrid Cloud Monitoring