Anomaly Detection and AI Ops
Explore methods for automated anomaly detection in your observability data. Get an introduction to AI Ops concepts for predictive insights.
Spotting the Unusual in Data
In system observability, an anomaly is any data point or pattern that deviates significantly from the expected behavior of your system. Think of it as a "red flag" that something might be wrong or changing.
Detecting these unusual events quickly is crucial for maintaining system health and preventing outages. It helps you find problems before they escalate.
Why Static Alerts Fall Short
Many traditional monitoring systems rely on static thresholds. For example, "alert if CPU usage > 80%". While useful, these often create noise or miss subtle issues.
- Systems are dynamic; what's normal at 2 PM might be abnormal at 2 AM.
- Seasonality and trends make fixed thresholds difficult to manage.
- They can't adapt to gradual changes or complex patterns.
Anomaly detection aims to be smarter and more adaptive.
All lessons in this course
- Correlating Logs, Metrics, Traces
- Anomaly Detection and AI Ops
- SLOs, SLIs, and Error Budgets
- The RED and USE Methods