I
Impetora
Production

AIOps

AIOps is the application of AI and machine learning to IT operations data, used to detect anomalies, correlate alerts, and automate incident response.

What is AIOps?

AIOps platforms ingest logs, metrics, traces, and tickets; cluster events; suppress noise; and surface probable root causes. The term is distinct from MLOps. MLOps is about operating ML systems; AIOps is about using ML to operate other systems. Modern AIOps increasingly combines classical anomaly detection with LLM-based summarisation and triage.

How does AIOps apply to enterprise AI?

Enterprises adopt AIOps to reduce alert fatigue, shorten mean time to resolution, and free staff from repetitive triage. It complements rather than replaces existing observability.

Related terms

Production

MLOps

MLOps is the discipline of operating machine learning systems in production: versioning, deployment, monitoring, retraining, and governance.
Production

Observability

Observability for AI is the ability to understand what an AI system did, why it did it, and at what cost, by inspecting its inputs, outputs, intermediate steps, and metrics.
Production

Model Drift

Model drift is the gradual or sudden degradation of a model's performance in production caused by changes in input data, target distribution, or operating context.

External references

Impetora

Need help applying AIOps to your enterprise? Submit a short brief and we reply within one business day.

Submit a projectBack to glossary
Discovery call

Book a discovery call

Tell us what you would like to build. We reply within one business day.

30-minute call. Free of charge. No obligation.