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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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I would argue that strong data management foundations are required to enable all AI/ML use cases
Majority of large scale multi sourced data is being referenced for ML models
I’m not sure if I understood your question but a use case would be model training/maintenance where you have to ensure data quality is high and there has to be governance around PII/PHI type data