What techniques can be used for debugging machine learning models in production?

Debugging machine learning models in production can involve several techniques including monitoring input data for anomalies, using performance metrics dashboards to identify issues, performing logging to capture model predictions and related features, and analyzing model behavior with tools like SHAP or LIME for interpretability. Also, maintaining a rollback strategy allows for immediate recovery from failures, while A/B testing can help assess changes in model performance post-deployment.