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