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Except that if their models have already inferred sufficient structure from your data to do this in a way useful for training, then their models have already solved the problem your models are trying to learn.


As a point of clarification it's not the model that is being abstracted, it's the raw data.

The use cases that this addresses are ones where you cannot train models in the same system/space/network as where the data exists.




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