When it comes to machine learning models, there are many differences between creation environments (where the model was built), and production environments (where the model will be used, monitored and have it's life cycle managed). The creation environment is oriented towards a specific set of people working on the model, with specific system, data and outputs configurations. But the production environment may be quite different - with other people, systems and requirements applied to the model. Understanding these differences allows organizations to be efficient in both environments, and know how to best navigate for the full life cycle of their critical machine learning assets. Let’s take a deeper look at the differences between creation and production environment in order to increase the effectiveness of our deployment process.