Skytree, Inc

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Skytree, Inc.
Venture-funded startup
Industry Machine Learning
Founded 2012
Headquarters San Jose, California
Key people
Martin Hack, President & CEO and Co-founder; Alexander Gray, CTO and Co-founder
Products Skytree Server
Website www.skytree.net

Skytree, Inc is a San Jose, California-based startup company that develops machine learning software for enterprise use. Skytree came out of stealth mode in February 2012. announcing Skytree Server, a machine learning system to discover patterns and make predictions from complex, massive data.

History[edit]

The company was co-founded by Martin Hack and Alexander Gray.[1][2]

In April 2013, Skytree raised $18 million in Series A funding.[3] The round was led by U.S. Venture Partners. Scott McNealy, co-founder and former chief executive at Sun Microsystems, also participated. Other investors include Javelin Venture Partners, UPS, and Osage University Partners.[4][5]

Technology and products[edit]

Skytree has machine learning methods that include: random decision forests, kernel density estimation, K-means, singular value decomposition, gradient boosting, decision tree, 2-point correlation, range searching, K-nearest neighbors algorithm, linear regression, support vector machine, and logistic regression.[6]

Skytree Server software operates in Linux on a single server computer or multi node cluster, and is intended for use by modelers for development of machine learning models, and for production deployments (in real time or batch usage). It is designed to connect with existing IT infrastructure. It can be configured to accept data streams and compute results from multiple sources. The resulting analytics are returned through the same channels.

Standard data sources include both structured and unstructured data from:[6]

Skytree Adviser software finds an appropriate model fit to given data, and explains its findings to the user in ordinary language.[7] It includes methods found in statistics packages like regression analysis, analysis of variance and cluster analysis. It provides a graphical user interface that emphasizes tasks (such as cluster, classify or compare) over algorithms and includes short explanations of the underlying statistical methods.[8]

Adviser reads file formats including files with the following extensions: TXT, CSV, DAT, GML, XML, NET, PDF, ODT, DOX. It can also read database files from MySQL, Oracle Database, Microsoft SQL Server, IBM DB2, and Teradata. The software runs on Mac OS, Windows, and Linux.[9]

Customers[edit]

Skytree customers include:[5][10]


See also[edit]


References[edit]

  1. ^ "Skytree". CrunchBase. CrunchBase. Retrieved 1 July 2013. 
  2. ^ Knapp, Alex. "Skytree Uses Machine Learning To Crunch Big Data". Forbes. Retrieved 9 July 2013. 
  3. ^ Delevett, Peter. "Wiretap: Tidbits From Scott McNealy, Khosla Ventures, Jive And The Lending Club Stunner". Silicon Beat. Retrieved 9 July 2013. 
  4. ^ Clark, Don (30 April 2013). "Skytree Looms in Big Data Forest with New Funding". The Wall Street Journal. Retrieved 1 July 2013. 
  5. ^ a b Vance, Jeff (12 June 2013). "10 Hot Big Data Startups to Watch". CIO. Retrieved 1 July 2013. 
  6. ^ a b Roy, Krishna. "Skytree takes root in the business of machine learning on large data sets". Impact Report. 451 Research. Retrieved 9 July 2013. 
  7. ^ Harris, Derrick. "USVP, UPS and Scott McNealy pump $18M into machine-learning startup Skytree". Gigaom. Retrieved 9 July 2013. 
  8. ^ Lorica, Ben. "Data Science Tools: Fast, easy to use, and scalable". Strata. Retrieved 9 July 2013. 
  9. ^ "Skytree Adviser FAQ". Skytree. Retrieved 1 July 2013. 
  10. ^ Hernandez, Pedro (1 May 2013). "Big Data Analytics, Machine Learning Help Skytree Land $18M". Enterprise Apps Today. Retrieved 1 July 2013. 
  11. ^ SETI. "SETI Institute Partners with Machine Learning Company, Skytree". SETI Institute. SETI Institute. Retrieved 1 July 2013. 
  12. ^ "Skytree and CANFAR Host Educational Webinar on How to Perform Machine Learning or Advanced Analytics on Big (Astronomical) Data Sets". Business Wire. 25 May 2012. Retrieved 2 July 2013. 

External links[edit]