Launch HN: FloydHub (YC W17) – Heroku for Deep Learning

Here’s an anecdotal story about how awesome AEther was (been a long time, so a little fuzzy on details): In 2011, Harry Shum was the VP of the Bing division at Microsoft. It was the early days of Bing (~10% market share, ~$2bn annual loss, etc.) - we had good talent, but were lagging behind Google in tech. In one of our all-hands meetings, Harry jokingly announced that if we beat Google in our core relevance metric (called NDCG), he’d take the entire Bing team, approx. 300 people strong, for a fully paid trip to Las Vegas.

Sure enough, a year later, Bing did beat Google in our core relevance metric (http://www.insideris.com/microsoft-bing-beats-google-in-the-...) and all 300 of us went to Vegas for a weekend as promised. (Spoiler: Google did eventually beat Bing back later)

The success and rapid acceleration in relevance gains was attributed in large parts to the introduction of a new tool called AEther (in addition to improving ML tech and hiring top talent). AEther was an experimentation platform for building and running data workflows. It allowed data scientists to build complex workflows and experiment in a massively parallel fashion, while abstracting away all the engineering concerns. I used it a ton on a daily basis and loved it. The AEther team claimed that it increased the experimentation productivity of researchers and engineers by almost 100X. Even now, when I ask ex-Bing data scientists working at other companies about what they miss the most from their time at Microsoft, AEther is almost always in the top 3 answers.

Having seen how awesome AEther was from the inside, one of our goals is to bring its benefits to the rest of the world as well. However, having talked to a few individual data scientists and researchers over the last month, their preference seems to be CLI over GUI (while bigger companies like it much better). May be its one of those things you have to get used to, or may be our implementation is clunky. So we’re making the GUI an enterprise only feature for now, while we continue to help individual data scientists through our CLI.

As you say, there's plenty of room out there. And we're all competing with Amazon, Google, Microsoft, etc. anyway. :-)

A couple of years ago I worked for a local cloud server provider, as a backend developer. Some of the work I did moved the company into deploying VPS instances using OpenStack. Our backend code was mainly PHP-based; so we used OpenCloud for the purpose - extending it where needed (when we started it didn't support all we needed; I extended things in such a way so that when we did need to upgrade OpenCloud, it would gracefully work without breaking anything - it was a gamble that I didn't know if it would really work - just had a hunch - 9 months in we upgraded, and it all worked perfectly).

Anyhow - at that time, seeing what we had available for servers and such (we were competing somewhat with DO) - I suggested we add support for GPU instances and maybe pivot toward an ML offering of some sort. Not gut our bread-n-butter, but offer up some kind of ML package for those that needed or wanted it.

I was shot down by management as it being too "pie in the sky" - not even demand or something like that. To be honest, I'm not even sure they understood what I was trying to convey, so maybe part of the problem was mine as well.

The company was eventually sold and I moved on, but seeing now how these kinds of services are in demand, I sometimes wonder on what "could've been". Ever since taking my first MOOC in ML (Ng's ML Class in 2011) - I've tried to interest employers in applying what (little) I know on the subject. I'm not an expert, but I'd love to apply my learning (on top of the 25+ years of software dev experience I already have). Today, I'm in the middle of the Udacity Nanodegree MOOC - I doubt much of that will transfer for my current employer, but maybe the general knowledge I'm getting of TensorFlow and Keras, among other bits, might help in the future.

I think, though, that FloydHub might be fun to play around with for future personal ML/DL projects as time goes on; I look forward to trying it out someday soon!