Let's say I have a basic regression model being used in production and now I want to implement periodical model retraining (i.e. once a month) where I take a batch of new data from last month and fit old model on this new batch with one epoch only.
Assuming that model is using MinMaxScaler as feature normalization mechanism, how should I proceed with scaling during such automated periodical retraining? Should I scale the data with old scaler, that was fitted on the initial training set or should I somehow fit the scaler again but if so, on what data? Only the newest batch or newest concatenated with the old initial training set?