I think the fact that machine learning so hyped is a problem; there's the Dunning-Kruger effect. A lot of people are entering the field, who don't understand ma

I think the fact that machine learning so hyped is a problem; there's the Dunning-Kruger effect. A lot of people are entering the field, who don't understand mathematics behind the algorithms, but they can make Keras models that run and train and seem to produce some promising results. They might not know about normalizing data, how to prepare data, how to deal with missing data, etc... In Academia and in "more reputable" projects, domain knowledge is vital to the success of an ML project. This doesn't seem to be the case for startups, looking to make some quick cash. Lots of online articles tout the lack of domain knowledge needed to create models... just googling "machine learning without domain knowledge" brings up a ton of articles saying machine learning is "easy" even without expert knowledge.