Competing on insight is fast becoming the new normal. The extraction of insight from data ushers in new opportunities to innovate faster, streamline costs, and deliver more customized customer experiences. And yet the barriers to efficient, rapid and scalable analytics are holding back enterprises of all sizes as they wrestle with everything from managing data sprawl to deploying new algorithms.
Kevin Huiskes and Radhika Rangarajan discuss Intel’s strategy to lower barriers to advanced analytics and AI, make results faster and more efficient, and enable data scientists and developers to make better use of existing infrastructure, emphasizing solutions based on the latest Intel Xeon Scalable platform and the open source framework BigDL, a distributed deep learning library for Apache Spark.
This session is sponsored by Intel.
Kevin Huiskes is the director of marketing in Intel’s Data Center Group. In his 16 years at Intel, Kevin has held a variety of senior business and marketing positions throughout the company, including two years as chief of staff to the executive vice president of Intel’s Data Center Group. His experience includes managing the Intel Data Center Group central marketing organization, managing the Intel Xeon processor E7 product line, business development, and a variety of other product management roles. Prior to Intel, Kevin served as a legislative assistant and committee aide to a member of congress in the US House of Representatives. He holds an MBA from Georgetown University and a BA in political science from Wheaton College.
Radhika Rangarajan is an engineering director for big data technologies within Intel’s Software and Services Group, where she manages several open source projects and partner engagements, specifically on Apache Spark and machine learning. Radhika is one of the cofounders and the director of the West Coast chapter of Women in Big Data, a grassroots community focused on strengthening the diversity in big data and analytics. Radhika holds both a bachelor’s and a master’s degree in computer science and engineering.
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