Presented By
O’Reilly + Cloudera
Make Data Work
29 April–2 May 2019
London, UK
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

16:35–17:15 Wednesday, 1 May 2019
LSTM-based time series anomaly detection using Analytics Zoo for Spark and BigDL
Data Science, Machine Learning & AI
 Location: Capital Suite 17
Secondary topics:  Deep Learning, Temporal data and time-series
Guoqiong Song (Intel)
Average rating: ***..
(3.40, 5 ratings)
Collecting and processing massive time series data (e.g., logs, sensor readings, etc.) and detecting the anomalies in real time is critical for many emerging smart systems, such as industrial, manufacturing, AIOps, and the IoT. Guoqiong Song explains how to detect anomalies in time series data using Analytics Zoo and BigDL at scale on a standard Spark cluster. Read more.