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5/ "Again- deferring huge, expensive computation to the users, hence avoiding a lot of complication on our end. And we’d argue it’s better for the users."
6/ "My hot take on “the cloud” approaches is that they tend to be the default approach to a fault and traditional desktop/local deployments tend to get overlooked even if they make the most sense."
Very thought-provoking. You're saying take advantage of designing with constraints in mind from the beginning? (make what you're making work on a 6s) Instead of defaulting to cloud's unlimited offerings?
In our case we were forced to think that way because of being 100% bootstrapped. I do really think that operating with constraints defined like that ahead of time really helps to spur creativity though. Cloud ML offerings can get really really expensive quickly.
I agree. Here is an interesting read I found which contains lots of insights about picking networks for mobile https://machinethink.net/blog/mobile-architectures/ …
The advantage of being on cloud is that it doesn't impose the users to have beefy systems to run this models, and putting it on cloud also is more feasible as you can spin up and shut down servers as you see the usage.
But cloud gpu memory is also very expensive, so makes sense to outsource and just work on perfecting your algorithms and models.
Think about it as providing a service. Scheduling your resources for users is more efficient than everyone having idling hardware. You also keep control over your source code which you can update without anyone noticing. The incoming unlabeled data helps to improve your models.
It's very, very expensive to do it as a cloud service. To the point where it completely changes what kind of business model you can offer. Control over code- do I really need that? I don't think so. And updates to the model only will happen every few months.
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