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> most people just want to drop a dataset and pick the type of model (say, multiclass image classification) and leave the rest for machines to optimize.

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.

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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.

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