Is there a ML data processing parameter getter and setter approach to wrap common ML pre-processing transformers / blocks / layers? [closed]
Closed 22 mins ago.
Is there a ML data processing parameter getter and setter library that wraps common ML pre-processing transformers / blocks / layers?
In a mchine lerning data pre-processing pipeline, the pipeline steps are normally serialised or saved as pickles or as layers in a model so they can be loaded again later for serving or predicting thereby preservving the transform / fit parameters of each step derived from the original training data.