I agree that it is exactly this. New tooling has made machine learning easier to use. As a result, people with deep domain knowledge but less machine learning e

I agree that it is exactly this. New tooling has made machine learning easier to use. As a result, people with deep domain knowledge but less machine learning expertise are starting to apply ML to the problems they understand that best. One of the biggest roadblocks to this happening more today is that people don't know how to perform feature engineering to prepare raw data for existing machine learning algorithms. If we could automate this step, it would be a lot easier for subject matter experts to use ML. For example, I work on an open source python library called featuretools (https://github.com/featuretools/featuretools/ https://github.com/featuretools/featuretools/) that aims automated feature engineering for relational datasets. We've seen a lot of non-ml people use it make their first machine learning models. We also have demos for people interested in trying it themselves: https://www.featuretools.com/demos https://www.featuretools.com/demos. I expect to see a lot more work in the automated feature engineering space going forward.