Surgery is a high stakes activity where carefully honed skills have a huge impact on a highly variable outcome. There are good reasons why surgical teams have well defined specialized roles.
These lessons apply to machine learning projects. Segmenting, professionalizing and optimizing the different aspects of data science projects will help us turn machine learning into a team sport.
Anand Sampat at Datmo provides a useful framework for thinking about machine learning projects:
1) Life Cycle
2) Responsibilities
Too often self-reliant data scientists will attempt to execute all these responsibilities themselves. That is an artisanal approach that will not scale up.
Instead we should have specialists who develop professional expertise at each of these roles, which will drive more efficient projects and higher quality results. This specialization will also be directly addressing the shortage of data scientists by allowing the ones we do have to focus on the aspect of their project that they are uniquely qualified to take on.