In venture capital circles, machine learning startups are about to catch fire. This makes sense as the size of data sets that companies and organizations need to utilize spirals beyond what the human brain can fathom.
As Derrick Harris at Gigaom reports, Skytree landed $18 million in Series A funding from US Venture Partners, United Parcel Service and Scott McNealy, the Sun Microsystems co-founder and former CEO. The company began just over a year earlier with $1.5 million in seed funding.
As big data gets bigger ever more quickly, machine learning makes it possible to identify meaningful patterns in real time that would elude sharp humans even with the best of query tools.
Still, there’s often a place for human judgment to flesh out the findings of machine learning algorithms.
For example: Netflix recommendations, the ZestFinance credit risk analysis platform and ProPublica’s Message Machine project that combs through vast volumes of crowd-sourced emails to find important news stories on a given topic.
The flagship Skytree product, Skytree Server, lets users run advanced machine learning algorithms against their own data sources at speeds much faster than current alternatives. The company claims such rapid and complete processing of large datasets yields extraordinary boosts in accuracy.
Skytree’s new beta product, Adviser, allows novice users to perform machine learning analysis of their data on a laptop and receive guidance about methods and findings.
As the machine learning space becomes more accessible to a wider audience, expect to see more startups get venture funding.
And with DARPA striving to make it easier for machine learning developers to focus more on application design and less on the complexities of statistical inference, this trend could have momentum for some time to come.