Getting data science projects into production is a real pain. Even with virtual environments and docker containers there is still a lot of friction in the deployment process.

Often code written by data scientists still needs to be wrapped with an API layer of some kind. There are a lot of people and a lot of effort involved in getting a data science project into production, which in my opinion is one of the main reasons for failure in such projects.