[D] Tabular Data: DL vs GBDTs on large scale datasets

I've been hearing lately that NNs are better than GBDTs when scaled up alot:

* Uber [https://www.uber.com/en-CA/blog/deepeta-how-uber-predicts-arrival-times/](https://www.uber.com/en-CA/blog/deepeta-how-uber-predicts-arrival-times/)
* Stripe [https://stripe.com/blog/how-we-built-it-stripe-radar](https://stripe.com/blog/how-we-built-it-stripe-radar)
* Most CTR papers coming from google are also NN based (like [https://arxiv.org/abs/2209.05310](https://arxiv.org/abs/2209.05310))
* Meta mentions NNs in their recommender system (also kind of a large scale tabular problem there) [https://engineering.fb.com/2023/08/09/ml-applications/scaling-instagram-explore-recommendations-system](https://engineering.fb.com/2023/08/09/ml-applications/scaling-instagram-explore-recommendations-system)
* Lyft forecasting [https://medium.com/this-week-in-machine-learning-ai/causal-models-in-practice-at-lyft-with-sean-taylor-1e62efd62385](https://medium.com/this-week-in-machine-learning-ai/causal-models-in-practice-at-lyft-with-sean-taylor-1e62efd62385)

What's your intuition on DL vs GBDT on (very)large-scale tabular datasets? Have you heard of other such examples (or the reverse)?

Are there any particularly …

data datasets examples hearing intuition machinelearning nns scale tabular tabular data test