Model Monitoring - AIQ Capability Overview
Model monitoring represents the activities necessary to ensure models are performing to specification and improving.
Model monitoring represents the activities necessary to ensure models are performing to specification and improving.
Live models often have problems if they aren't monitored on a regular basis resulting in a poor experience for end users. This includes on-going evaluation of the model against ground truth, analyzing model exhaust, and retraining models to improve performance, reduce drift and avoid bias.
Drift means a degradation in model performance as data distributions change over time.
Model error analysis is performed to explore, understand, and isolate errors in model predictions, as well as the sources of those errors. This allows data scientists and data engineers to diagnose root causes and take corrective action.
Model monitoring represents the activities necessary to ensure models are performing to specification and improving.