Threshold alerts are great for known failure modes, but they miss slow drift and correlated signals.

A practical anomaly detector starts with clean metrics, stable baselines, and a deployment plan that avoids alert storms.

We recommend using a multivariate model (e.g., Isolation Forest) on CPU, memory, disk I/O, and network signals—paired with simple guardrails like maintenance windows and burn-in periods.

Start small: pick one service, add observability, then iterate on features and alerting thresholds using incident reviews.

Key Takeaways

• Catch degradation earlier
• Reduce noisy alerting
• Improve incident response quality

Anomaly detection works when you treat it like a product, not a model.