Continuous Model Monitoring, without manual tracking
Models evolve as data, behaviour and assumptions change. Yields supports continuous model monitoring to maintain reliable model oversight.
Model monitoring is often reactive and fragmented.
After deployment, model performance is hard to control.
As a result, models may continue to drive decisions even when their behaviour has changed.
How model monitoring works with Yields
Define monitoring metrics
Select performance, stability and data quality indicators for each model.
Run monitoring cycles
Record monitoring results consistently across models and time periods.
Review trends and thresholds
Compare outcomes to detect drift, deterioration or threshold breaches.
Trigger follow up actions
Use monitoring results to support review, revalidation or governance decisions.
Keep a clear audit trail
All monitoring evidence, outcomes and actions are stored in one place.
Ready to strenghten your model monitoring?
See how Yields helps organisations monitor models continuously using its Model Risk Management software.
FAQ
Model monitoring is the continuous process of tracking a model's performance, stability, and data quality over time to ensure it remains reliable and accurate after deployment. This is essential because models can evolve as data, behavior, and assumptions change, which can lead to performance drift.
Model monitoring is often reactive and fragmented. This leads to issues such as tracking results in scattered tools like spreadsheets, inconsistent metrics, unclear thresholds, and delayed detection of problems, especially the faster drift seen in AI and machine learning models.
Yields provides its Model Risk Management software to bring structure, oversight, and continuous monitoring to the process. This helps teams identify monitoring issues earlier through structured metrics, consistent thresholds, and clear oversight across all models.
Yields applies the same consistent performance framework to both traditional and AI-influenced models. This ensures consistent oversight even as methodologies change and helps manage the faster behavior changes and drift common in modern AI and machine learning models.