O'Reilly、Cloudera 主办
Make Data Work
2017年7月12-13日:培训
2017年7月13-15日:会议
北京,中国

周五欢迎致辞 (Friday opening welcome)

此演讲使用中文 (This will be presented in Chinese)

Ben Lorica (O'Reilly Media), Doug Cutting (Cloudera), Jason (Jinquan) Dai (Intel)
09:00–09:05 Friday, 2017-07-14
地点: 紫金大厅A(Grand Hall A)

大会日程主席 Ben Lorica、Jason Dai 与 Doug Cutting致辞开始第一天主题演讲。

Photo of Ben Lorica

Ben Lorica

O'Reilly Media

Ben Lorica is the chief data scientist at O’Reilly Media. Ben has applied business intelligence, data mining, machine learning, and statistical analysis in a variety of settings, including direct marketing, consumer and market research, targeted advertising, text mining, and financial engineering. His background includes stints with an investment management company, internet startups, and financial services.

Photo of Doug Cutting

Doug Cutting

Cloudera

Doug Cutting is the chief architect at Cloudera and the founder of numerous successful open source projects, including Lucene, Nutch, Avro, and Hadoop. Doug joined Cloudera from Yahoo, where he was a key member of the team that built and deployed a production Hadoop storage-and-analysis cluster for mission-critical business analytics. Doug holds a bachelor’s degree from Stanford University and sits on the board of the Apache Software Foundation.

Photo of Jason (Jinquan) Dai

Jason (Jinquan) Dai

Intel

Jason (Jinquan) Dai is a senior principal engineer and CTO of big data technologies at Intel, where he is responsible for leading the global engineering teams (located in both Silicon Valley and Shanghai) on the development of advanced big data analytics (including distributed machine and deep learning), as well as collaborations with leading research labs (e.g., UC Berkeley AMPLab and RISELab). Jason is an internationally recognized expert on big data, cloud, and distributed machine learning; he is the program cochair of the O’Reilly AI Conference in Beijing, a founding committer and PMC member of Apache Spark, and the creator of BigDL, a distributed deep learning framework on Apache Spark.

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