The examples I reviewed in the documentation seem to describe how to define, share and reproduce a train model pipeline. Once we are happy with our trained model and we want to move it into production, what would be the recommended approach to use DVC to ensure the pipeline consistency between train and predict?
I would like to re-use the DVC pipeline defined for training (feature engineering, processing,…) to ensure consistency and proper usage. On the other hand, the pipeline would also be somewhat different (each individual model script would “predict” instead on “training”).
Is there a recommended solution ? Should I create additional variables to tell each script whether to train or predict ? What to make of the metric at the end?