One of the primary challenges of any ML/AI project is transitioning it from the hands of data scientists in the develop phase of the data science lifecycle into the hands of engineers in the deploy phase.
Where in the life cycle does data scientists’ involvement end? Who takes responsibility for the operationalized models? And how long should the transition between development and deployment last? What does a data scientist do, compared to a data engineer or a DevOps engineer?
The answers to these questions are seldom cut and dried, even in a small shop. For an enterprise, the questions can become even more complicated as you add additional team members, each with different roles, into the mix.