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This episode is part of the greater AI/ML conversation I had with Zhamak. To start, Zhamak recognizes we aren't where we want to be in terms of capabilities - ways of working or tooling - to make this a reality just yet. But, if we can make it so data scientists can trust and easily consume from data products - that we create data products that don't care what use case type - regular analytics or AI/ML - can we remove a lot of the complexity they face? Do they need feature stores for data they aren't transforming? If they can get continued access and know the quality, why create a separate process that has fragility instead of trust the data product owners upstream?

I wasn’t smart enough in the moment to talk about do we need to have a copy of the training data itself for reproducibility but folks smarter on ML than I am can answer that one, probably in the affirmative. But overall, there is a lot of complexity in the way we do AI/ML because data scientists can't trust the sources of their data and they feel the need to take control because if they don't, their models break. So we need to earn their trust and show them a better way. But again, we aren't there yet, so let's work to make this a reality in the future.

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