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I've heard that we should use train dataset's scale for that of test data so they are in line with each other in terms of scale.

And I know we use transform() function for the test data preprocessing.

But I am wondering how I can apply the same logic to the real-life data set after we successfully save and load a model since I don't think we can use transform() function anymore.

From what I understand, there must be a way to "extract" mean and standard deviation from the train data set and to apply it for real-life model when using the model we saved already, but how can we do that?

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Ideally, the transform operation is part of your pipeline, therefore, if you have reallife data, with the same pipeline, it will apply the same transformation.

(I'm assuming you're using a modeling language that makes use of pipelines)

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It has to be saved somewhere i.e. Database after the training is done.
The saved values should be used on new data and all these steps should work in a loop i.e. when you re-train again the values will be updated and saved again.

e.g. If we see the Keras pre-trained models, it provides the necessary pre-processing function. We can directly use that

from keras.applications.resnet50 import preprocess_input
train_datagen = ImageDataGenerator( preprocessing_function=preprocess_input)
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