Whetlab, a startup founded by some former colleagues of mine, provided a service just like this 4 years ago (in fact, it's referenced in the Vizier paper as its open-source variant Spearmint), but unfortunately, it was acquired and shut down by Twitter: https://venturebeat.com/2015/06/17/twitter-acquires-machine-...
Black-box optimization is a hugely important problem to solve, especially when experiments require real wet-work (i.e. medicine, chemistry, etc.). Kudos to Google for commercializing this - I expect it will see a lot of use in those fields. But it's bittersweet to know that it's taken this long for this type of application to be promoted like this.
Yeah SigOpt clearly has taken the space left by Whetlab. They must not be too pleased about this announcement, I mean generally the worst thing that can happen to your startup is a tech giant launching in your space. Then again the problem of hyperparameter/black-box optimization is so ubiquitous that there should be enough space for them both.
I wasn't familiar with SigOpt - this is really cool. I actually think SigOpt will see this as great publicity telling people about the usefulness of the concept - and whereas Google won't lift a finger for (insert hedge fund or pharmaceutical giant here) beyond maintaining uptime, SigOpt can provide those customers with customized advice about how to integrate, what pitfalls to watch for, how to design experiments to maximally take advantage of their technology.
And they're competitive with Spearmint (though not necessarily the closed-source versions of it used at Whetlab), though Vizier remains to be seen: https://arxiv.org/pdf/1603.09441.pdf
Thanks! I'm one of the co-founders of SigOpt (YC W15).
You hit the nail on the head. We've been trying to promote more sophisticated optimization approaches since the company formed 4 years ago and are happy to see firms like Google, Amazon, IBM, and SAS enter the space. We definitely feel like the tide of education lifts all boats. Literally everyone doing advanced modeling (ML, AI, simulation, etc) has this problem and we're happy to be the enterprise solution to firms around the world like you mentioned. We provide differentiated support and products from some of these methods via our hosted ensemble of techniques behind a standardized, robust API.
We're active contributors to the field as well via our peer reviewed research [1], sponsorships of academic conferences like NIPS, ICML, AISTATS, and free academic programs [2]. We're super happy to see more people interested in the field and are excited to see where it goes!
Black-box optimization is a hugely important problem to solve, especially when experiments require real wet-work (i.e. medicine, chemistry, etc.). Kudos to Google for commercializing this - I expect it will see a lot of use in those fields. But it's bittersweet to know that it's taken this long for this type of application to be promoted like this.