Great article! (author's book on ML are very good, greatly recommended).
Even though Deep Learning on Tabular data is a topic that is picking up interest, I'd try to stay away from it as much as possible. The main advantage of "statistical methods" (in contrast to Deep Learning) is the interpretability.
Most business applications of Machine Learning happen on "tabular" data (explicit features) with a lot of knowledge by the company about the features selected.
A simple Decision Tree gives you AMAZING accuracy and you can still understand what's going on behind the model.
Interpretability in Business ML applications is, IMO at least, the single most important trait of the model selected.
Fully agree! In the case that tabular data means business process records, there's absolutely no need to use anything more complicated than a principled statistical model. Focus on the quality of the data and the business problem, not exotic ML.
In more detail: the business data is prone to: evolving processes / systems / products / markets / customers; errors / omissions / corrections; tail events; hirings / firings; data loss; etc etc. The datasets tend to be small, messy, complicated, subjective. Nothing about this suggests needing a large, complicated model.
This is such an odd dogmatic generalization. There's plenty of business cases where e.g. SHAP is enough for explainability and having a higher accuracy is important.
SHAP is a recent de-facto standard way to get feature importance out of any model, whether you care about how much each column in your dataset contributes to a given decision or say which pixels in an image mattered the most.
Even though Deep Learning on Tabular data is a topic that is picking up interest, I'd try to stay away from it as much as possible. The main advantage of "statistical methods" (in contrast to Deep Learning) is the interpretability. Most business applications of Machine Learning happen on "tabular" data (explicit features) with a lot of knowledge by the company about the features selected.
A simple Decision Tree gives you AMAZING accuracy and you can still understand what's going on behind the model.
Interpretability in Business ML applications is, IMO at least, the single most important trait of the model selected.