Model Monitoring

Model Monitoring is the continuous observation of deployed AI systems to detect performance degradation, drift, anomalies, and operational issues. A complete monitoring program covers technical metrics (latency, throughput, error rates, cost), quality metrics (accuracy, calibration, fairness), drift metrics (data, concept, prediction distribution), and safety metrics (content-policy violations, jailbreak attempts, hallucination rates). Tools include Arize, WhyLabs, Fiddler, Evidently AI, Datadog AI, New Relic AI, and built-in monitoring in major MLOps and LLMOps platforms. Real-world monitoring practices include dashboards reviewed during ops meetings, automated alerts to on-call engineers, weekly model-health reports to product teams, and quarterly executive-level summaries. AI governance, AI compliance, and AI risk management frameworks require continuous monitoring — the EU AI Act mandates post-market monitoring for high-risk AI systems and ISO/IEC 42001 requires monitoring as part of the AI management system. Responsible AI cannot exist without operational monitoring across long-running enterprise AI deployments at scale.

Centralpoint Unifies Model Monitoring Across Your AI Estate: Oxcyon's Centralpoint AI Governance Platform monitors every model interaction across OpenAI, Gemini, Llama, and embedded options. Centralpoint meters consumption, keeps prompts and skills on-prem, and embeds monitored chatbots into your portals via one JavaScript line.


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