monitor-ml-drift

Data drift in production ML models is detected by applying statistical tests like PSI, KS, and Chi-square to compare production prediction logs against the training reference set, identifying when input feature distributions shift significantly.

You can track ML model performance with delayed ground-truth labels using Confidence-Based Performance Estimation (CBPE). This technique estimates model performance metrics to ensure persistent performance monitoring even without immediate feedback.

Automating retraining triggers in MLOps pipelines is best achieved by implementing automated health checks that monitor for input data drift, prediction drift, and performance decay, automatically signaling when a model requires retrains.

Yes, this drift detection approach works with evidently and nannyml as dependencies, leveraging these libraries to monitor production models for train-serve skew detection and robust model observability.

Champion-challenger loops for model monitoring work by validating that a challenger model demonstrably outperforms the current production version before promotion, ensuring automated governance and preventing the deployment of stale predictions.

You should not rely solely on statistical tests for ML model monitoring when ground-truth labels are consistently delayed, as pure drift detection cannot estimate performance decay without techniques like CBPE for delayed-label estimation.