AIOps applies machine learning to IT operations, reducing alert fatigue and accelerating incident response. Core capabilities: 1) Intelligent alerting: ML correlates alerts from 100s of monitoring tools, groups related alerts into incidents, reduces alert noise by 80-90%. Tools: Moogsoft, BigPanda, Dynatrace AIOps. 2) Root cause analysis: ML models analyze telemetry (logs, metrics, traces) during incidents to identify probable root causes. Correlation of deployment events, infrastructure changes, and anomaly detection. Mean time to resolution reduction: 50-70%. 3) Log analysis: LLM-powered log parsing and semantic search. Ask natural language questions: "What was happening in the auth service 5 minutes before the incident started?" Elastic, Splunk, and Grafana all adding LLM-based log querying. 4) Capacity planning: time series forecasting predicting resource needs 30-90 days ahead, automatic scaling recommendations. Prevents over-provisioning (cost) and under-provisioning (performance). 5) Self-healing: predefined remediation playbooks triggered automatically when specific anomaly patterns detected. "If memory usage > 90% for 5min, trigger scale-up and notify on-call." 6) Change risk analysis: ML assesses deployment risk based on code diff size, files changed, recent related incidents, team experience. Flags high-risk deployments for additional review. Implementation: start with alert correlation, measure alert noise reduction, then add root cause analysis.