Data scientists, regardless of what package they use, are used to training machine learning models to solve business issues. The classic approaches to creating Data Science, such as the CRISP-DM cycle, support this approach. But the reality is that a great model can never simply be put into production. A model needs the data prepared and surfaced to it in production in exactly the same way as when it was created. And there may be other aspects involved with the use of the model and the surfacing of results in the correct form that the model itself does not intrinsically have in it.