No, the feature would have to be in the online feature store (NDB, not Redis in our case). We have metadata about the schema of the model (the same schema as its training dataset) so that the applications can just supply the IDs and get back the feature vector in the correct order and get it also in the format needed (a npy array or a tfrecord example). This really helps maintenance, as adding a new feature means you don't have to update the application to account for the new feature and ensure the ordering of features matches the ordering in the training dataset.