How Do You Know It's Broken?

ML Anomaly Detection and Automated Runbooks At intermediate, monitoring is static thresholds. The follow-up: 'Static thresholds break on seasonal data, holiday spikes, and organic growth. How do you handle that?' The trap is saying 'we adjust the thresholds manually.' The senior answer is ML-driven anomaly detection that adapts to patterns humans can't manually encode. The simplest ML anomaly detector that actually works in production is seasonal decomposition (STL): separate the time series into trend, seasonal, and residual components, then alert on residuals exceeding 3 standard deviations. This handles weekly patterns, monthly spikes, and gradual growth without manual threshold updates. Say: 'I'd use STL decomposition - it handles 80% of seasonal patterns with minimal tuning.' The foll