# AIOps AIOps (Artificial Intelligence for IT Operations) is the application of AI and machine learning to automate and enhance IT operations. The goal is to reduce manual effort, accelerate incident detection, and enable predictive — rather than reactive — operations. Coined by Gartner in 2017, AIOps sits at the intersection of big data, ML, and IT operations management. ## Core capabilities - **Anomaly detection** — Identify unusual patterns in logs, metrics, or events before they become incidents - **Root cause analysis** — Automatically correlate signals across systems to pinpoint failure sources - **Event correlation & noise reduction** — Filter alert storms down to actionable signals - **Predictive analytics** — Forecast capacity needs, failure likelihood, or performance degradation - **Automated remediation** — Trigger self-healing actions in response to known failure patterns ## AIOps vs traditional monitoring | | Traditional Ops | AIOps | |---|---|---| | Alert source | Rule-based thresholds | ML-based anomaly detection | | Incident response | Manual triage | Automated correlation + suggestions | | Capacity planning | Historical averages | Predictive models | | Alert volume | High noise | Filtered, prioritized | ## Relationship to DevOps and MLOps - AIOps augments [[DevOps]] by injecting intelligence into the ops feedback loop - It is distinct from **MLOps**, which focuses on operationalizing ML model pipelines — AIOps uses ML to operate *any* IT system ## Common tools - Dynatrace, Datadog, Splunk, BigPanda, Moogsoft ## References - ## Related - [[DevOps]] - [[Large Language Models (LLMs)]] - [[CI CD pipelines]] - [[AI and Jobs]] - [[Rachel Woods]]