AI Glossary: MLOps & AIOps

AI Ops

Artificial intelligence for IT Operations: applying AI to IT telemetry (logs, metrics, traces, events) to detect incidents, correlate root cause, predict capacity, and automate remediation.

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AIOps observability

Observability enhanced by AI (noise reduction, anomaly detection, causal correlation) to speed MTTR.

AIOps ServiceNow

Forrester AIOps

Forrester AIOps typically refers to Forrester’s reports that score vendors on core AIOps functions such as event correlation & noise reduction, anomaly detection, topology/service mapping, incident & runbook automation, cloud/k8s monitoring, and ITSM/DevOps integrations. For leaders, it’s a way to align stakeholders on what “good” looks like, define "must-have" versus "nice-to-have" features, and benchmark total cost and roadmap maturity. Use it to frame RFPs, validate a shortlist, and pressure-test vendor claims against independent criteria.

ML Ops

Machine Learning Operations: The practices and toolchain to build, deploy, monitor and govern ML/GenAI reliably (pipelines, registries, CI/CD, evaluations, observability, rollback).

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MLOps stacks

Typical toolchains (feature store, training, registry, serving, monitoring, evals) used to implement MLOps.

Moogsoft AIOps

Moogsoft ingests alerts and metrics from monitoring tools, applies correlation, clustering, and anomaly detection to suppress duplicates, and groups related signals into a single incident context. Typical strengths include pattern detection, topology awareness, and workflows for collaboration (“war room” style) plus integrations with ITSM tools for auto-ticketing. Best fit if your pain is alert fatigue across hybrid infra and you need fast MTTA/MTTR gains without ripping out existing monitors. Look for: breadth of integrations, data volume handling, and how easily runbook automation plugs in.

Pagerduty AIOps

If you already use PagerDuty for paging and incident response, its AIOps layer can dedupe and correlate alerts, enrich them with context (service ownership, recent changes), trigger automation/runbooks, and route to the right team based on service maps. Strengths are end-to-end incident lifecycle (detect → triage → automate → resolve → postmortem) and native on-call workflows. It’s attractive for organizations wanting a single pane for signals, responders, and process automation. Assess: quality of correlations on your data, change/event correlation (e.g., with CI/CD), and cost vs. incremental benefit over your current monitoring stack.

Predictive AIOps

Using forecasting models to anticipate incidents or saturation and take preventative actions.