[platypii]

There's a clear trend in the industry to increasingly rely on cloud services, so it seems reasonable that machine learning would follow the same trend. As long as the compute is in the same data center, data transfer is rarely the bottleneck for these kinds of deep learning algorithms, which is why we designed algorithmia to be able to operate anywhere -- on all the major cloud providers, as well as on premise.

[vonnik]

Right, but the question is: Whose cloud and what kind of cloud? Are we talking private cloud, virtual private cloud? Who manages it? Even saying "as long as the compute is in the same data center" is a huge assumption. I think it's great that Algorithmia can go operate anywhere. How do you do that? What do you need to operate well on prem?

[neurotech1]

At "enterprise" level there is a lot of interest in Hybrid Clouds, because on premises is still a requirement.

[agibsonccc]

(Disclaimer: I'm biased, I'm vonnik's cofounder):

I agree that most startups need to get an MVP out the door as soon as possible which leads to clouds. I think hybrid cloud will be the way to go long term.

If you think about it, on one side we have things like AWS and others where devops and "make running your own infra at scale easy" like docker and k8s. On prem in some form isn't going anywhere. What WILL be interesting are the plays like say: convox where you can manage a cloud like you would an on prem openstack/k8s deployment.

[visarga]

Can they anonymize the data before receiving it from their clients? It would be a great advantage to be able to use their service in a privacy-conscious way.