I would like to have a keras model self-contained to reduce the training/serving skew.
It would mean here having a preprocessing layer that is doing essentially what MinMaxScaler from scikit learn is doing to have data ranging from 0 to 1.
I know about the normalization layer, but I dont want to normalize my inputs but to scale it to 0-1 interval.
Rescaling layer is closer to what I need but it seems like it requires all the inputs features being on the same scale at the beginning (like all features being between 0 and 255 for images). However, I have got features on different scales.
How can I add that kind of preprocessing layer in my model to have the preprocessing within the model?