I dunno. Machine Learning as a Service seems like a tough thing to monetize, as most machine learning in practice involves a lot of tweaking which would then imply that practitioners would like to go further down the stack to work directly with an R, Python, ?? module, look at its code, see where it's failing, working etc.<p>I do like Machine Learning as a Service as a loss leader. e.g. customer walks up to the door, can't really get the problem cracked with an out of the box solution, but instead you sell/him her on an expensive long-term consulting project. i.e. the IBM Model.<p>Does anyone know how one of the pioneers in the segment, Numenta, is fairing? They've been around for a while and seem to have recently changed their name to Grok Solutions.<p>Deep Learning as a service seems like something that could work as their are less knobs for the user to fiddle with. That being said, it does not seem like Deep Learning is quite there yet.
I know of at least one Berkeley ML PhD. that was working on a startup that could have easily used the slogan, "Machine Learning as a Service".<p>I have to wonder how founders/founders-in-the-making react when faculty members from their alma mater, from their own department no less, enter their space. Must be a little bit like having Google enter your niche.
To keep ML fast and cheap, you want to keep compute as close to data as possible.<p>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.
What IP are they patenting? It sounds like they just implemented random forests in C++ in a way that avoided data copy, which is obviously what you'd want to do for performant non-parametric machine learning... So what's patentable here?
How does this compare to, say, the Google Prediction Service[1] ? The example use-cases sound similar-ish to my layman interpretation. I've never used a ML service though, so honestly I have no idea what to expect or look for.<p>Obviously this offers an on-site option that Google does not, which might open up other realms of options. I'm mostly curious in how / how well / what range of problems they're capable of.<p>[1] <a href="https://developers.google.com/prediction/" rel="nofollow">https://developers.google.com/prediction/</a>
Couple of points:<p>- Benchmarking against Weka, R, and Python is not exactly pitting your product against stiff competition. Skytree (skytree.net) is another company in the same space with the same focus. Benchmarking against them would be interesting.<p>- I thought it was amusing that in a company of < 20 people the five founders thought it necessary to adopt such grandiose titles: CEO (fine), CTO, Director of Engineering, Chief Scientist, Director of Data Science (exactly how are the hairs split between these four?)
Is this algorithm (a Random Forest variant named WiseRF) difficult to tune or easy? Does the user have to guess parameters, learning rates and such?<p>I've found that ML algorithms can be like race cars - you really have to know their quirks to get performance out of them. The opposite would be analogous to a luxury car - almost everything is taken care of by the computer - you don't shift gears and don't open the lid.<p>So, is this WiseRF a race car or a luxury car?
So, at a high level, some future version of this software will show intuitive visualizations of learned models of the data? Or currently, at a lower level, it seems to be implementations of standard machine algorithms with a Python API to use them.<p>There seems to be an emphasis on efficiency, although I don't think that most freely available machine learning libraries are fundamentally poorly implemented. One problem with these libraries is that documentation can sometimes be scarce. Another problem, for the 0.01% of companies which actually have "big data", is that they might not scale, whatever that means.<p>Regardless of the library used, one of the bigger problems may be that machine learning, if it's worth it at all, is inherently fickle and tricky. To make an overly broad conjecture: if an externally provided machine learning solution works well, either your data didn't require that much domain knowledge to understand (it was "obvious") or some external/outsourced firm has a deeper understanding of your data than you do. More of the former type of analysis might not necessarily be a bad thing, though.
One tiny bit of my MSc thesis was based on Google Prediction API and it worked fine, apart the non-conventional URLs used that caused problems with .NET networking.<p>Even in a project where ML is more important, I think it's usable...