I have a background in mathematics and am currently studying Machine Learning in a Master's program. Soon after I started I came to the same conclusion as the a

I have a background in mathematics and am currently studying Machine Learning in a Master's program. Soon after I started I came to the same conclusion as the author of the article. It is becoming very easy for someone with some basic coding ability and shaky mathematical foundations to do some online courses, emulate a few tutorials or maybe even sign up for a bootcamp and, voilà! 6 months later they are more than competent enough to quickly put together ML solutions that are close to an 'optimal' solution which requires much more in-depth knowledge and engineering time. Close enough that the quality/amount of data and feature engineering (basically, domain knowledge gained through general problem solving skills rather than ML skill) is far more important than the details of any model. This is why, when I start applying for jobs soon, I plan to maintain and frame my skill set as a quantitative problem solver, not an ML specialist (as I had thought I would do when I started this Master's degree). Soon the value of being able to build a good ML model will decline, but hopefully the knowledge and skill set of those with a proper training and understanding of this field (mathematical ability, computer science/programming ability and problem solving ability) will still be valuable.