Machine learning is everywhere these days as companies and organizations find themselves trying to make sense of data sets far too large and complex for the human brain alone. On Tuesday, Skytree cashed in on the hype with with an $ 18 million Series A round led by U.S. Venture Partners along with delivery giant UPS and Sun Microsystems co-founder and former CEO Scott McNealy. Skytree launched in February 2012 with $ 1.5 million in seed funding.
Machine learning is such a hot topic right now because data volumes are becoming so large and complex that humans alone can’t query their ways through them fast enough or intelligently enough to spot latent patterns among the mess of data. It’s the algorithmic engine that powers a bunch of Google services and your Netflix recommendations, as well as web content-curation service Prismatic and alternative-underwriting platform ZestFinance. As we covered in some detail at this year’s Structure: Data conference, machine learning is particularly powerful when its ability to correlate tens of thousands of variables is paired with human judgment about what really matters.
Skytree co-founder Alexander Gray (second from left) at Structure: Data 2012. (c) Pinar Ozger
Skytree, for its part, sells a product called Skytree Server that lets users run a wide variety of machine learning algorithms across whatever data they have. It might be an oversimplification, but Skytree is essentially a souped-up version of statistical-analysis packages like SPSS or SAS that’s designed to run fast — and, more importantly — without sampling across a scale-out server architecture. In March, the company also rolled out the beta version of a new product called Adviser that can run on a laptop and walks more-novice users through the analysis of their data, including what methods were used and why, and whether the findings are statistically significant.
I suspect we’re just seeing the opening salvo in what will be a rush to fund machine learning startups over the next couple of years. Skytree is among a number of increasingly promising startups in the space, including (but certainly not limited to) Ayasdi and Quid. As more individuals see the promise of machine learning and get skilled in applying it to their particular problems and datasets — as UPS apparently has — it could become become one of the go-to analytic methods in the big data era.
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