What is AIOps?

Summary

AIOps applies AI and machine learning to IT operations to analyze logs, metrics, traces, events, alerts, and related system data. It detects anomalies, correlates events, uses topology and dependencies to identify likely root causes, and supports guided or automated responses. The guide positions AIOps between observability and action, reducing noise and adding context without replacing observability, DevOps, or human judgment. Its workflow includes data ingestion, normalization, anomaly detection, event correlation, root-cause analysis, automation and orchestration, and collaboration workflows, with human-in-the-loop review recommended for higher-risk production changes. It contrasts domain-centric and domain-agnostic approaches and recommends evaluating coverage, depth, integrations, setup effort, and organizational fit before expanding automation.

Takeaways

1. 1

   AIOps learns normal behavior across time periods instead of relying only on static thresholds, then alerts when observed behavior deviates from the baseline.

2. 2

   Event correlation groups related alerts, while topology-aware analysis distinguishes a likely degraded root service from downstream symptoms.

3. 3

   Higher-risk production actions should use human-in-the-loop review; lower-risk predefined responses can be recommended or automated.

FAQ