Predictive Cloud Infrastructure Monitoring: ML-Powered Anomaly Detection with Zero False Positives

Most cloud monitoring tools generate too many false alerts, which leads to alert fatigue and actual incidents getting missed. We built an ML pipeline that hits zero false positives in production while still catching 99.7% of real issues. It combines transformer-based time series analysis with graph neural networks that map infrastructure dependencies. We tested it across 1 billion+ data points from different cloud environments.