It's neat to see training/evaluating/serving ML models get commoditized (eg Amazon's new service). But the hard part is feature generation.
@snoble I think the first problem is just to make it trivial to *express* features like that, produce training sets, and track the values.
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@avibryant I think that's right. Once you have a grammar you can navigate the space way better -
@avibryant so we have ways to convert m dimensions to scalers (random forests, logistic regression)... -
@avibryant ... and a way to roll up streams of scalers (algebird). Just need a way to optimize combinations
End of conversation
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