Furthermore, as a VP of data science, selling the business on the value and benefits of data science is non-trivial as many are not even aware of what's capable with data science so my personal opinion is that ML isn't quite suitable for mainstream work. Practitioners still need to have a strong grasp of the business context, data features and nuances, etc. which still is quite technical in nature. Asking a data analyst to build models with auto ML is something I consider to be a leap too far and is risky to put into production. The only way it could work is to have an experienced data scientist supervise.
If you work in Google & you can remove the political blockers & guarantee the model’s space of deployed resources is parametrizable with a clean set of parameters that AutoML can consider as part of the optimization, then by all means use it.
That’s just decades away from being viable at any given product company.
That appears to be the business model of https://www.datarobot.com/
automated tools with consulting data scientists to help clients use them effectively
I wonder how automatic machine learning tools like these will shape the "data science" roles in the future. Obviously, the most cutting edge research will always be done by specialised human experts, but perhaps tools like these will lower the bar required for the bulk of mainstream ML work.