Following ML best practices, I use Scikit Pipelines to make sure my data preprocessing is the same at each model development iteration.
Also as a best practice, once I have completed model development I retrain the best model with the chosen hyperparameters on the entire dataset.
Now, in order to prepare for a deployment to production, I am trying to understand if I should export the model chosen itself, or the entire Pipeline object? I would want to apply the same exact preprocessing steps in production, correct?