Model Monitoring
The ongoing process of tracking an AI model's performance, data quality and operational metrics after deployment to detect degradation, drift or anomalies.
In plain language
Keeping constant watch on your AI after it goes live; tracking its accuracy, speed and behaviour. Like a car dashboard that alerts you when maintenance is needed.
Why this matters
Continuous model monitoring is essential for AI governance. Without it, you have no visibility into whether deployed systems perform safely and fairly in production.
Relevance
ImplementationMonitoring requirements for every deployed model must be defined in governance policies and implemented through MLOps infrastructure.
Related terms
Putting model monitoring into practice in your organisation?
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