Model Performance Monitoring
Tracking and evaluating the performance of your model during testing
What is Model Performance Monitoring?
Model performance monitoring is the process of tracking and evaluating the performance of machine learning models. It helps ensure models are accurate, reliable, and effective. Model monitoring is needed to identify factors causing models to change over time including: data distribution changes, training-serving skew, data quality issues, and environmental shifts.
All models deployed in our hosting environment are monitored while they are tested in the clinical setting at UMHS. We work closely with our members to ensure that deployed models are working optimally while they are being tested.
Did you know?
Monitoring your model enable you to analyze the accuracy of the prediction and allows you to tweak the model to optimize performance.
“After my model was deployed and and running with prospective data, Digital Health Innovation provided the tools needed to see the data flowing into the model and even tracked the performance of the model.”
Brahmajee Nallamothu, MD, MPH,
Co-Director, U-M Digital Health Innovation
Professor, Internal Medicine, Medical School
