Building deep learning-powered big data
Radhika Rangarajan explains how Intel works with its users to build deep learning-powered big data analytics applications (object detection, image recognition, NLP, etc.) using BigDL.
| Talk Title | Building deep learning-powered big data |
| Speakers | Radhika Rangarajan (Intel) |
| Conference | Strata Data Conference |
| Conf Tag | Making Data Work |
| Location | London, United Kingdom |
| Date | May 23-25, 2017 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
AI plays a central role in the today’s internet applications and emerging intelligent systems, which are driving the need for scalable, distributed big data analytics with deep learning capabilities. There is increasing demand from organizations to discover and explore data using advanced big data analytics and deep learning. BigDL is a distributed deep learning library built on Apache Spark to address the needs for running deep learning workloads on big data clusters, which was developed inside Intel and open sourced to the community in December 2016. Radhika Rangarajan explains how Intel uses BigDL to build deep learning-powered big data analytics applications (object detection, image recognition, NLP, etc.). This session is sponsored by Intel.
