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Thought this was interesting. Across 160 teams of researchers, just about all failed to make good life outcome predictions on things like GPA, evictions, layoffs, and others. Data followed 4.5k families across 15 years, with 13k features (varied over time). Haven't looked at it directly yet, but will be turning the docs and data inside out... In the meantime, authors claim this as showing the limits of ML. Oh, and it's published in PNAS, so you know there's some big publication energy there.
https://www.pnas.org/content/117/15/8398
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Coach
Google tables(?), h2o auto ai, comet ml, data robot, auger ai, and tpot should probably make the list if you are looking for automl type stuff.
I've played around with h2o, comet and auger but none of them were that useful for me. Of the ones I've tried h2o feels the most feature complete.
Coach
It will come down to whatever the client has invested in, so it would be good to get a refresher on GCP vs AWS vs Azure tools in general for modeling.
This is more for when the client is looking for recommendations on what to implement