[apohn]

One of the ways I think about this type of problem is by asking "You want to use computation to extract a signal from this data. What's that signal worth to you in business ROI dollars?"

If Domain Expertise + Feature Engineering + ML model can get you 90% of the way there and it runs on a tiny cloud instance that takes 30 minutes to train, is a DL based approach that pushes you to 91% worth it from an ROI instance if takes a 4xGPU cluster 2 days to train it, not to mention inference costs? Especially if you need to explain what the model is doing?"

This above is exactly the situation I'm in now with my job. I'm on the "Get useful stuff to production so we can save money" side of things, and we have R&D teams who try to approach the same problems using DL and all the latest methods. At least for the use cases our team focuses on, they haven't been able to do more than set $$$ on fire via GPUs. For us, Domain Knowledge + Good Data Engineering is the secret.

I think ML is going to be around for a long time because it works, even though DL is dominating the news right now. Just because a neurologist can also diagnose and treat common medical conditions (e.g a pneumonia), that doesn't mean we need every doctor to be neurologist.

[Sanej]

this is awesome! thanks