What is Continuous Model Evaluation?

Success metrics include system uptime, model performance stability, deployment velocity, and operational cost efficiency.

Use proxy metrics that correlate with model quality: prediction distribution stability, feature drift detection, confidence score trends, and user behavior signals like click-through rates. Compare model outputs against a periodically refreshed gold standard dataset labeled by domain experts. For some use cases, delayed ground truth arrives naturally, for example fraud labels come in 30-90 days. Combine multiple proxy signals for a composite health score that tracks model quality between ground truth updates.

You need a metrics pipeline that logs predictions and outcomes, a scheduled job that calculates evaluation metrics daily or weekly, a dashboard showing metric trends against baselines, and automated alerts for significant degradation. Open-source tools like Evidently AI or custom scripts with Prometheus and Grafana handle this well. Budget 3-5 days of engineering effort for initial setup. The infrastructure pays for itself by catching degradation weeks earlier than manual review.

Use statistical process control charts that establish normal variance bands from historical data. Apply change detection algorithms like CUSUM or ADWIN that are designed for sequential monitoring. Require sustained degradation over 3-5 evaluation windows rather than alerting on single-point drops. Account for known patterns like weekday versus weekend variation. Set different sensitivity levels for different metrics based on business impact.

Use proxy metrics that correlate with model quality: prediction distribution stability, feature drift detection, confidence score trends, and user behavior signals like click-through rates. Compare model outputs against a periodically refreshed gold standard dataset labeled by domain experts. For some use cases, delayed ground truth arrives naturally, for example fraud labels come in 30-90 days. Combine multiple proxy signals for a composite health score that tracks model quality between ground truth updates.

You need a metrics pipeline that logs predictions and outcomes, a scheduled job that calculates evaluation metrics daily or weekly, a dashboard showing metric trends against baselines, and automated alerts for significant degradation. Open-source tools like Evidently AI or custom scripts with Prometheus and Grafana handle this well. Budget 3-5 days of engineering effort for initial setup. The infrastructure pays for itself by catching degradation weeks earlier than manual review.

Use statistical process control charts that establish normal variance bands from historical data. Apply change detection algorithms like CUSUM or ADWIN that are designed for sequential monitoring. Require sustained degradation over 3-5 evaluation windows rather than alerting on single-point drops. Account for known patterns like weekday versus weekend variation. Set different sensitivity levels for different metrics based on business impact.

Use proxy metrics that correlate with model quality: prediction distribution stability, feature drift detection, confidence score trends, and user behavior signals like click-through rates. Compare model outputs against a periodically refreshed gold standard dataset labeled by domain experts. For some use cases, delayed ground truth arrives naturally, for example fraud labels come in 30-90 days. Combine multiple proxy signals for a composite health score that tracks model quality between ground truth updates.

You need a metrics pipeline that logs predictions and outcomes, a scheduled job that calculates evaluation metrics daily or weekly, a dashboard showing metric trends against baselines, and automated alerts for significant degradation. Open-source tools like Evidently AI or custom scripts with Prometheus and Grafana handle this well. Budget 3-5 days of engineering effort for initial setup. The infrastructure pays for itself by catching degradation weeks earlier than manual review.

Use statistical process control charts that establish normal variance bands from historical data. Apply change detection algorithms like CUSUM or ADWIN that are designed for sequential monitoring. Require sustained degradation over 3-5 evaluation windows rather than alerting on single-point drops. Account for known patterns like weekday versus weekend variation. Set different sensitivity levels for different metrics based on business impact.