Term
Model Monitoring
Definition
Model monitoring is a feature that tracks the performance of artificial intelligence models when they are actively used to catch any changes or mistakes that could affect how well they work.
Where you’ll find it
This feature is typically located within the dashboard or management section of an AI platform. It often appears in more advanced plans or versions that focus on operational capabilities.
Common use cases
- Ensuring an AI system remains accurate and reliable over time by detecting any deviations or deterioration in performance.
- Alerting users automatically when there are significant changes in data that could indicate an issue.
- Gathering data for analysis to improve the efficiency and accuracy of AI models.
Things to watch out for
- Initial setup complexity: The process can be challenging without familiarity with the monitoring tools.
- Over-reliance on automated alerts: Users should manually review reports to fully understand AI performance.
- Delay in detection: Sometimes, there may be a lag between an issue occurring and its detection by the monitoring system.
Related terms
- Drift
- Alerting
- Data Science
- AI Operations
- Performance Metrics
Pixelhaze Tip: Start with default monitoring settings to understand what typical alerts and performance statuses look like for your model. Gradually adjust thresholds and parameters to finetune alerts to your specific needs.