cortex-eval

To evaluate model performance and detect drift, you compare current production metrics against established baselines. This process analyzes data shifts and error patterns across segments to identify model degradation and inform remediation strategies.

Monitoring a deployed classifier for regression requires comparing post-training metrics against previous baselines. By analyzing prediction drift and error patterns on production data, you can confirm if retraining improved performance or introduced new failures.

Error analysis on production data helps debug ML model degradation by isolating failure modes across specific data cohorts. It reveals underlying feature shifts and environment changes causing performance drops, enabling targeted remediation.

You can validate model behavior across different data segments by applying an evaluation workflow to production data. This analyzes performance variations and prediction drift across cohorts to ensure consistent system reliability.

Comparing baseline vs current performance for ML monitoring requires production metrics that capture prediction accuracy and data distributions. Analyzing these metrics highlights data drift and error patterns, generating a structured report of system health.

Deployed ML model performance degradation often stems from data drift or environment changes in production. Comparing current metrics against baselines and analyzing feature shifts across cohorts identifies the specific failure modes causing the regression.