> Its true that deep learning often makes feature engineering moot. however, a lot of deep learning projects takes machine learning engineers and applied scient

> Its true that deep learning often makes feature engineering moot. however, a lot of deep learning projects takes machine learning engineers and applied scientists along with a host of other support engineers + hardware costs DL's reduction of the required feature/model engineering is a big deal for difficult problem domains where the cost of a mistake is low. That doesn't mean you don't still benefit from adding development resources, it's just that the development cost/performance tradeoff is still typically better than with a similar-performing explainable solution. I hope this will change in the future. > ..., logistic regression with l1 penalization and only a little bit of feature engineering often does really well,... While I agree that Lasso is far more explainable than DL, its explainability rapidly degrades as the useful feature dimension increases and it requires significant feature engineering for good performance on difficult problems.