What do you think about data teams as bottlenecks? As in, the processes humans are following to do data engineering and AI/ML work

I’ve seen some practices by keepers of the medical data you mention (data vendors and providers), and trust me, there’s some serious work to do with DataOps in that space - which will serve to reduce bottlenecks in systems, and unlock more data to train on, responsibly

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Oh yeah, totally. It's part of what makes some of domains such a pain. Difficulty of sourcing the data, whether it's fundamental (just hard/expensive to do), or bureaucratic/annoying (bad data, understaffed data teams), or has tons of legal barriers... all of it contributes to defensibility. This is, of course, all under the context of the keepers as you mention. If you're looking for medical data, the landscape of standardization despite all of the FHIR efforts, etc. is pretty bad, and they certainly don't have enough people or inclination to do the clean-up.

If you're an AI startup, though, having good data engineering and folks doing AI/ML work is kind of table stakes. I know there's been a lot of talk about talent shortages, but that's always been the case in tech, and hasn't been unique to AI. It makes salaries sky high (relatively speaking), but certainly doesn't stop the companies.

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