Skytree Platform

The leading enterprise-grade machine learning platform for big data

built-for-big-data

To build more accurate models in a shorter amount of time, you need high performance algorithms and to utilize all of your data. The Skytree platform is built for algorithmic speed and scales to perform machine learning on massive amounts of data – structured and unstructured – so data scientists never have to downsample.

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enterprise-grade

Because it’s designed for enterprise companies, Skytree is highly scalable and delivers on the requirements of demanding technology teams with enterprise-grade features such as model management, LDAP & SSL secure user access, high availability model training, full model audit trails and Kerberos secured user data.

easy-to-adopt

Skytree allows you to leverage your investment in big data by installing on existing infrastructure and by implementing a platform that includes data preparation, automated machine learning model building and easy model deployment. You access the platform via an intuitive GUI or programmatic interface.

Why Skytree

Built to scale to the largest and most diverse data sets while delivering the greatest predictive model accuracy, Skytree empowers data scientists to build more accurate models faster. The result? Deep analytic insights and the ability to predict future events, make recommendations, and reveal untapped markets and customers.

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HIGHEST ACCURACY Skytree simplifies the data prep process and uses the entire dataset, including structured and unstructured data, to run more experiments and identify high-value patterns.

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AUTOMATION We take advanced analytics to the next level using artificial intelligence to produce sophisticated algorithms, model training and automated experimentation.

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ENTERPRISE-GRADE Our end-to-end platform fits seamlessly into your big data infrastructure with auto-documentation, model management, visual interpretation, and user and data security.

 

"We have been working on fraud detection since 1990 and now have a 10% lift using Skytree. That’s a big deal."

 

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Calendar

May 24, 2015
May 24th, 2016
On May 24, 2016 at 6:30 PM, Nick Ball, Ph.D and Staff Data Scientist at Skytree, will walk through the model building process, including use case, data preparation, featurization and results, used to accurately predict the distances of galaxies using the Sloan Digital Sky Survey (SDSS). While Nick’s use case is of an astrophysical nature, the approaches, methodology and algorithms can be used to build models in a wide range of industries and use cases. Join us at Skytree's San Jose Headquarters: 1731 Technology Drive Suite 700 San Jose, CA 95070.

Resources

Solution Sheets Analyst Reports White Papers Webinars and Videos