[nil-sec]

If with AutoML you mean substantial architecture search (as your question seems to suggest) then the answer is it’s feasible only for the largest players in the field. It’s possible to use some tricks but even then your standard cluster with a few hundred GPUs isn’t going to cut it for large scale problems. That said, any problem that e.g. google deems valuable enough can be meta-optimized to oblivion. Same holds if you have a huge pile of cash and think a few percent performance boost is worth it.

[ptrenko]

So AutoML is not smart yet? Its just about throwing more compute?

[nil-sec]

Nothing is smart yet. What people understand under automl is different but I’m referring to meta learning here, be it the architecture, the optimizer, the learning rate etc. Optimizing these things requires an outer loop in addition to training the architecture and for architecture search in particular this boils down to training lots of models and evaluating all of them. There are ways of making this search a bit better than random search with techniques such as regularized evolution (essentially evolution with a bias for younger individuals) or other tricks but all of them require huge compute resources.