“The advent of “auto-ML” — software that provides methods and processes for creating machine learning code — has led to calls to “democratize” data science and AI. The idea is that these tools enable organizations to invite and leverage non-data scientists — say, domain data experts, team members very familiar with the business processes, or heads of various business units — to propel their AI efforts.
Ideally, mentors are involved throughout the AI product lifecycle, from the concept phase all the way through to model maintenance. At earlier stages, mentors can help teams avoid significant pitfalls and ensure a robust roadmap is developed. In later stages, they can play a more tactical role, like when the team needs guidance with a deployed model that isn’t performing as well as anticipated. Indeed, this function can also be very useful for experienced data scientists. Novice and expert data scientists alike can benefit from having an expert sounding board. It’s important to stress here that potentially two kinds of mentors are needed: one to solve for technical and business risks, the other to ensure compliance with the AI ethics or a Responsible AI program.”