Re:Google's AutoML: Cutting Through the Hype
by halflings on Thursday August 02, 2018 @12:53AM
(#17669300)
Frankly, I don't think this is obvious.
Case in point: sequential models (granted, probably not yet included in the first batch of AutoML). There are so many ways to model the problem that tweaking each of these ways, in a way that makes sense for your dataset, takes a lot of work.
I've built models that worked fairly OK, only to have a colleague build a separate model that had +10% prAUC by virtue of adding some additional mechanism (say, attention, a different RNN cell, more units, etc.).
I'll also say that this is aimed towards people that are unfamiliar with ML and would have trouble finding and re-implementing the state-of-the-art in their specific field.
I think the disconnect here is that you can reuse existing architectures and get state-of-the-art performance without running something like AutoML. It's not clear that creating a bespoke architecture for your specific problem is always better, let alone always a good use of your resources.