Modern systems produce more metrics than any single person can reason about. As systems grow and change, defining fixed thresholds becomes harder and unexpected behavior often appears without clearly crossing an alert boundary.
This talk looks at anomaly detection as a complementary way of working with metrics. Instead of relying on predefined limits, anomaly detection focuses on identifying behavior that deviates from what is normally observed over time.
Using a live walkthrough with real metric data, we show how anomalies can surface gradual changes, unusual patterns, and subtle shifts that are easy to miss in dashboards. The focus is on how developers can interpret these signals, where anomaly detection is useful and where it is not.
This session is exploratory and practical, aimed at developers who work with metrics and want additional ways to understand system behavior without introducing complex models or heavy tooling.