AIOps interview prep

• AIOps — Applying ML to operational telemetry — anomaly detection, alert correlation, root-cause assistance, capacity f
• AIOps and SRE — AIOps augments Site Reliability Engineering by automating SLO burn rate alerting, reducing toil below 30%, and
• AIOps Maturity Model — The AIOps maturity model describes a four-stage progression from fully manual operations to fully automated AI
• AIOps Meets MLOps — MLOps governs the full lifecycle of machine learning models from training through deployment and retraining, w
• AIOps ROI & Maturity — How to measure AIOps value and where organizations sit on the maturity curve — Gartner AIOps maturity model, b
• AIOps Root Cause Analysis — How AIOps systems decompose root cause analysis into failure localization, failure category classification, an
• AIOps Telemetry Pipelines — AIOps telemetry pipelines ingest and normalize five signal types -- metrics, logs, traces, events, and topolog
• Alert Correlation & Grouping — When one incident produces a thousand alerts via cascading dependencies, on-call engineers cannot triage. Corr
• Anomaly Detection in Ops — Algorithms for catching metric deviations without static thresholds — 3-sigma and ESD on stationary series, ST
• Capacity Forecasting — ML approaches to predicting resource needs before they become incidents — Prophet for seasonal decomposition,
• Chaos + Observability — How chaos engineering and AIOps intersect — steady-state hypothesis definition, using AIOps to validate that f
• Cloud-Native AIOps — Cloud providers offer native AIOps services that use ML to detect anomalies, surface probable root causes, and
• Event Management and CMDB — Event management and CMDB integration form the foundation of AIOps by transforming high-volume raw signals int
• Kubernetes AIOps — AIOps patterns specific to Kubernetes — kube-state-metrics for cluster health, Prometheus Operator ServiceMoni
• LLM SRE Agents — How LLM-powered agents operate in SRE — autonomous hypothesis-testing loops, hypermodal AI combining predictiv
• LLM-Powered AIOps — Large language models address the two core structural limits of traditional AIOps ML models -- inability to ge
• ML-Assisted Incident RCA — Tools that surface candidate root causes during incidents — pattern matching to past incidents, change correla
• Open-Source AIOps — Building AIOps capability without vendor lock-in — Prometheus anomaly detector (Fourier + Prophet), Grafana th

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