AIOps
AIOps is the application of statistical and machine learning techniques to operational data for detection, correlation and noise reduction.
What is aiops?
AIOps is the application of statistical and machine learning techniques to operational data for detection, correlation and noise reduction.
Plain and technical
AIOps uses pattern recognition on your monitoring data to spot unusual behaviour, group related alerts together and reduce the number of notifications people receive.
AIOps typically covers anomaly detection against learned baselines, event correlation to collapse related signals into single incidents, and log pattern extraction. It is distinguished from agentic systems by not planning or executing multi-step work: it detects and correlates rather than investigating and acting.
What it looks like in practice
Nearby vocabulary
Agentic AI
Agentic AI describes software that decides for itself which steps to take towards a goal, rather than executing a sequence written out in advance.
Monitoring
Monitoring is the practice of collecting predefined signals from a system and alerting when they leave their expected ranges.
Observability
Observability is the property of a system that allows its internal state to be understood from the signals it emits, including for failures nobody anticipated.
Anomaly detection
Anomaly detection identifies measurements that deviate significantly from a metric learned normal behaviour, rather than comparing against a fixed threshold.
How DevOpsArk handles aiops
Articles on this subject
AI agents in DevOps: where they help and where they do not
A practical assessment of where AI agents genuinely improve infrastructure work, where they are oversold, and how to introduce them without creating a new class of incident.
Monitoring vs observability: a distinction worth keeping
The difference between monitoring and observability, why the distinction is more than marketing, and what each one is actually for.
More definitions
See these concepts in a running system
A 30-minute walkthrough against your own infrastructure rather than a slide about the theory.