I added this to the definition to cover your example: Feature data leakage is when you include a feature when training a model, but that feature is not availab

I added this to the definition to cover your example: Feature data leakage is when you include a feature when training a model, but that feature is not available at inference time. You should, hopefully, discover feature data leakage when you try to write your inference pipeline and realize the feature is not available. The use of a feature store, that informs you whether your feature will be available for online models or only offline models, should help prevent you from training models with feature data leakage.