A model can perform well during validation and still struggle after deployment. Changes in customer behavior, data distributions, upstream pipelines, or the way predictions are used can all affect real-world performance.
The difficult part is identifying the actual cause rather than immediately retraining the model or changing algorithms.
Suppose a churn model has strong validation results, but its precision and recall deteriorate after deployment. Would you investigate data drift, label delays, training-serving inconsistencies, threshold selection, or changes in the customer population first?
What monitoring signals have helped you identify the root cause in a real project?