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Comment by ozten | Hacker News Reader

To keep ML fast and cheap, you want to keep compute as close to data as possible.

I don't think a web service would be broadly applicable. Perhaps in certain domains it would make sense, but bandwidth costs and duration would be a huge factor in most solutions.

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I completely disagree. I've used siftscience (http://siftscience.com) and the service is remarkably good. I admit there are some cases where it would be impractical. However, in my experience ML-as-a-service has been pragmatic and I've seen it lead to very favorable outcomes with minimal effort from the client.

So... instead of disagreeing out of hand, how did you overcome the fact that you had to pay for bandwidth between your terabytes of data and the computation centers?

Terabytes are cheap compared to my time. I've also never required that much training data, so perhaps your use case does not lend itself to these services. I've built automated ML model training systems myself with TreeNet and Mahout, as well as used API-based ML systems, and I'm comfortable saying there is a strong case to be made for services which save much unnecessary effort.

Storing terabytes is cheap. Terabytes of bandwidth is very very expensive.

Some old links on the cost of terabytes of bandwidth http://josephscott.org/archives/2009/01/how-much-does-one-te...

And that assumes you're willing to host with those providers, many of whom don't provide computing power. Also, the machine learning service you are using has to pay again for that bandwidth too and that cost gets passed along to you.

Transferring 1TB at a sustained rate of 1MB/s will take about 11.5 days.

One easy way to get around bandwidth costs is to just have the clients of your service communicate directly (via Javascript or other client-side code) with your "machine learning as a service" (MLaaS? :D) vendor. Of course this model only works for certain businesses and allows learning on certain things, but you can go a pretty long way with this.

Yes - but doesn't that just beg for putting it on S3 and spinning up EC2 instances on demand? Amazon already handles mailing hard drives to them and has the download bandwidth if you've got the upload speed.

The other option is doing it all in-house, the storage, the servers, oh yeah, and that whole ML thing. Not a bad thing, but not necessarily your core-competency.

If the data sets are already in the cloud, for example at AWS, you could skip the transfer costs if the machine learning SaaS operates on the same cloud.

In case dealing with terabytes of data it would probably make sense for the MLaaS operator to run points-of-presence in major clouds.

Ozten, we completely agree which is why we also provide an on premise version of our Machine Intelligence Engine. Our mission is to democratize ML and allow companies to easily deploy it in production.

Thanks for the interest and feedback!

Joey Richards, Chief Scientist, wise.io

That seems to be the approach of skytree, another entrant in this space.

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