A model performs well in testing but poorly in production. What could be wrong?

Common causes: data drift (production data differs from training data over time), train/serve skew (features computed differently in training vs serving), data leakage (a feature in training secretly encoded the label and isn't available at prediction time), an unrepresentative test set, or feedback loops where the model's own actions change the data. Diagnose by comparing training vs production data distributions, auditing feature pipelines for consistency, and checking for leakage. This is really an MLOps question about monitoring and pipelines, not just modelling.