Well, most practical ML algorithms (liner/logistic regressions etc) should be in the wheelhouse of most engineers. I read a comment somewhere "Its much easier t

Well, most practical ML algorithms (liner/logistic regressions etc) should be in the wheelhouse of most engineers. I read a comment somewhere "Its much easier to teach data-science to an data-engineer than vice versa" :) I think, if we data-engineers can step up the conversation from frameworks/databases to end-to-end use case, we will get more respect (and everything that comes with it like budget) from the rest of the org.