Cloud outages rarely begin when dashboards turn red. They start quietly — through subtle latency shifts, noisy logs, partial control-plane failures, and mismatched signals across layers. Yet most DevOps teams still rely on provider incident alerts or siloed monitoring to detect failure, losing precious minutes during high-impact events. This talk introduces a practical, real-world approach to predicting outages before they become customer-visible using multi-signal correlation across cloud platforms and Kubernetes environments. Drawing from millions of health events across Azure and large-scale distributed systems, we’ll break down how modern cloud-native infrastructure reveals early failure signals long before official incident notifications.

Attendees will learn: Why logs, metrics, traces, and alerts fail to detect outages early How to correlate cloud provider health signals with resource-level events How synthetic probes uncover failures dashboards don’t show Why “telemetry divergences” often predict outages 3–7 minutes early Practical steps to build a lightweight, proactive outage-detection pipeline Real failure stories from production and what we learned How to design reliability “guardrails” without adding operational overhead