Presented By
O’Reilly + Cloudera
Make Data Work
March 25-28, 2019
San Francisco, CA
Guoqiong Song

Guoqiong Song
Software Engineer, Intel

Website

Guoqiong Song is a senior deep learning software engineer on the big data technology team at Intel. She’s interested in developing and optimizing distributed deep learning algorithms on Spark. She holds a PhD in atmospheric and oceanic sciences with a focus on numerical modeling and optimization from UCLA.

Guoqiong Song是英特尔大数据技术团队的高级深度学习软件工程师。 她拥有加州大学洛杉矶分校的大气和海洋科学博士学位，专业方向是数值建模和优化。 她现在的研究兴趣是开发和优化分布式深度学习算法。

Sessions

1:30pm–5:00pm Tuesday, March 26, 2019
Analytics Zoo: Distributed TensorFlow and Keras on Apache Spark
Data Science, Machine Learning & AI
 Location: 2009
Secondary topics:  Deep Learning, Temporal data and time-series analytics
Jason Dai (Intel), Yuhao Yang (Intel), Jiao(Jennie) Wang (Intel), Guoqiong Song (Intel)
Average rating: ***..
(3.00, 6 ratings)
Jason Dai, Yuhao Yang, Jennie Wang, and Guoqiong Song explain how to build and productionize deep learning applications for big data with Analytics Zoo—a unified analytics and AI platform that seamlessly unites Spark, TensorFlow, Keras, and BigDL programs into an integrated pipeline—using real-world use cases from JD.com, MLSListings, the World Bank, Baosight, and Midea/KUKA. Read more.
4:20pm–5:00pm Wednesday, March 27, 2019
User-based real-time product recommendations leveraging deep learning using Analytics Zoo on Apache Spark and BigDL
Data Science, Machine Learning & AI
 Location: 2016
Secondary topics:  Deep Learning, Retail and e-commerce
Luyang Wang (Restaurant Brands International), Jing (Nicole) Kong (Office Depot), Guoqiong Song (Intel), Maneesha Bhalla (Office Depot)
Average rating: ****.
(4.00, 2 ratings)
User-based real-time recommendation systems have become an important topic in ecommerce. Lu Wang, Nicole Kong, Guoqiong Song, and Maneesha Bhalla demonstrate how to build deep learning algorithms using Analytics Zoo with BigDL on Apache Spark and create an end-to-end system to serve real-time product recommendations. Read more.