Presented By O'Reilly and Cloudera
Make Data Work
Dec 4–5, 2017: Training
Dec 5–7, 2017: Tutorials & Conference
Singapore

Bringing deep learning into big data analytics using BigDL

Xianyan Jia (Intel), zhenhua wang (JD.com)
12:05pm12:45pm Thursday, December 7, 2017

Who is this presentation for?

  • Solution engineers, data scientists, and machine learning software engineers

Prerequisite knowledge

  • A basic understanding of Apache Spark and deep learning

What you'll learn

  • Learn how to develop fast prototypes with BigDL's off-the-shelf deep learning toolkit and build end-to-end deep learning applications with flexibility and scalability using BigDL on Spark

Description

Deep learning is rapidly becoming one of the most successful and widely applicable sets of techniques in use today. But while cutting-edge deep learning research is emerging with breathtaking speed, there is often a gap between papers and prototypes—and an even larger gap between prototypes and production.

BigDL provides scalable deep learning functionalities on Apache Spark and native support for Spark ML pipelines for scalable deep learning training and inference. Xianyan Jia and Zhenhua Wang explore deep learning applications built successfully with BigDL, including neural recommendations, fraud detection, object detection with SSD, speech recognition (DS2), and 3D medical imaging analysis. You’ll learn how to develop fast prototypes with BigDL’s off-the-shelf deep learning toolkit and build end-to-end deep learning applications with flexibility and scalability using BigDL on Spark.

Photo of Xianyan Jia

Xianyan Jia

Intel

Xianyan Jia is a software engineer at Intel, where she’s responsible for developing deep learning and machine learning algorithms and pipelines. She is also a contributor to BigDL, a distributed deep learning framework on Apache Spark.

Photo of zhenhua wang

zhenhua wang

JD.com

Zhenhua Wang is a software engineer on JD.com’s AI and big data team, where he works on algorithm research and development for machine learning and computer vision, focusing on image feature representation, large-scale image deduplication, and search.