[psyklic]

"classical ML" is not very different from NNs. For example, basic NNs are essentially logistic regressors chained together. And NNs are evaluated and trained very similarly as simpler models (gradient descent, log loss, etc).

NNs also often perform similarly or worse than simpler models when you have "medium-sized" (and/or tabular) data. In fact, I nearly always start with simpler models when consulting -- why immediately make it complicated if a smaller, more interpretable model works well?