SESSION

Deep Learning to Big Data Analytics on Apache Spark Using BigDL

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With the continued success of deep learning techniques, there’s been a rapid growth in applications for perception in many modalities, such as image classification, object detection and speech recognition. In response, Intel’s BigDL is an open source distributed deep learning framework for Apache Spark that includes rich deep learning support and Intel Math Kernel Library acceleration, allowing users to quickly develop deep learning applications with extremely high performance on their existing Hadoop ecosystems.

This sessions will explore several key deep learning applications that Intel successfully built on top of Apache Spark with BigDL. Hear about the technologies they developed and what they learned from building such applications, including: the tool stack in the system and design considerations; an application on image recognition and object detection (faster-rcnn using VGG and PVANET); and an application on speech recognition with deep speech and acoustic feature transformers. He’ll also share other insights and experiences Intel gained while building a unified data analytics platform with Apache Spark MLlib and BigDL.

Session hashtag: #SFds10

Xianyan Jia, Software Engineer at Intel

About Xianyan

Xianyan is a Software Engineer at Intel. Her interest lies in big data and deep learning. She is currently developing deep learning pipelines such as Faster-RCNN and SSD with Bigdl (Intel deep learning library for spark).

Yuhao Yang, Software Engineer at Intel

About Yuhao

Yuhao Yang currently is a software engineer at Intel, focusing on providing implementation, consultant and tuning advice on Hadoop ecosystem to industry partners. His area of focus is distributed machine learning, especially large-scale analytical applications and infrastructure on Spark. He’s also an active contributor of Spark MLlib (50+ patches), delivered the implementation of online LDA, QR decomposition and several transformers of Spark feature engineering, and provided improvements on some important algorithms.