Also known as: Training serving Skew
Training-serving skew is a mismatch between the data, features, assumptions, or processing used during model training and those used during production serving. It can cause a model that performs well in training or validation to behave poorly after deployment.
The difference between a model's performance during training and that same model's performance during serving.