I've seen in several Tensorflow/Keras tutorials that data augmentation functions are added as keras layers. When I converted my Keras Python model (for production purpose) to TensorflowJS I faced the issue that e.g. the RandomFlip layer is not available in TensorflowJS. So I have to use the ImageDataGenerator.
I don't understand why should I put the data augmentation layers at all in the model, I mean when I want to use my model in production I'll still have them in my model which doesn't make sense for me.