[mumblemumble]

> Cynically, neural networks are easier as you don't really have to think about your model. Give some examples with some classes and you're done.

This way of thinking about it leads directly to things like statistical redlining.

It's also not specific to neural networks. I take a similar approach with logistic regression. Except that I like to replace the "and you're done" step with, "and you're ready to analyze the parameters to double check that the model is doing what you hope it is." Even when linear models need some help, and I need to do a little feature engineering first, I find that the feature transformations needed to get a good result are generally obvious enough if I actually understand what data I'm using. (Which, if you're doing this at work, is a precondition of getting started, anyway. IMNSHO, doing data science in the absence of domain expertise is professional malpractice.)

There is no, "and you're done" step, outside of Kaggle competitions or school homework. Because machine learning models in production need ongoing maintenance to ensure they're still doing what you think they're doing. See, for example, https://research.google/pubs/pub43146/