FICO, a predictive analytics and decision management software company, will host a predictive analytics competition with Kaggle, a company that runs predictive analytics competitions, in the FICO Analytic Cloud, a web-based infrastructure for creating, customizing and deploying analytics-driven applications and services.
The analytics competitions will begin later this summer and give data scientists, including Kaggle's community of 180,000 members, an opportunity to solve analytic challenges in the insurance, financial services, retail, life sciences and other industries. Contestants will have free access to the FICO Solution Stack, FICO's platform for big data analytics and decision management, in addition to a chance to win cash prizes and professional recognition.
"When you challenge a community of data scientists to solve data problems through an objectively judged competitive framework, you get solutions that are fresh, powerful and elegant," said Karthik Sethuraman, head of analytic solutions at Kaggle. "By partnering with FICO, we'll be able to present our community members with an even wider array of real-world analytic problems from FICO's diverse client base."
FICO said its Analytic Cloud application is built on open-source technologies and leverages industry standards, including Apache Hadoop, an open-source software framework; PMML, an XML-based language for predictive modeling; R, an open source analytic programming language and environment; Apache Lucene, a search engine library; and Apache Tika, a content analysis toolkit. The FICO Analytic Cloud is designed to support data transportability and open modeling best practices, FICO said.
"Crowdsourced innovation is an extremely exciting outgrowth of what we refer to as the democratization of analytics," said Doug Clare, VP for cloud analytics at FICO. "Together with Kaggle, we're eager to see what happens when some of the world's greatest minds get their hands on some of the world's most powerful analytic tools and applications in the FICO Analytic Cloud."
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