Presented By O'Reilly and Cloudera
Make Data Work
September 25–26, 2017: Training
September 26–28, 2017: Tutorials & Conference
New York, NY

NoScope: Querying Videos 1000x faster with Deep Learning

Daniel Kang (Stanford University)
Data science & advanced analytics, Machine Learning
Location: 1A 06/07 Level: Advanced
Secondary topics:  Deep learning

Who is this presentation for?

Data engineer, data scientist, manager

Prerequisite knowledge

Basic familiarity with data science

What you'll learn

Attendees will better understand how to analyze real-world video datasets at scale.

Description

Video is one of the fastest-growing sources of data with rich semantic information and advances in deep learning have made it possible query this information with near-human accuracy. However, inference remains prohibitively expensive: the most powerful GPU cannot run the state-of-the-art at real time. In response, we present NoScope, which run queries over video 1000x faster.

NoScope achieves such speeds by exploiting redundancies in video: temporal, environmental, and query-specific redundancies. I will explain how we exploit these redundancies and explain how these concepts can be generalized.

NoScope is a new project in the Stanford InfoLab under Professors Matei Zaharia and Peter Bailis. John Emmons and Firas Abuzaid also contributed to the work. The code is open-sourced: https://github.com/stanford-futuredata/noscope

Photo of Daniel Kang

Daniel Kang

Stanford University

Daniel is a PhD student in the Stanford InfoLab supervised by Peter Bailis and Matei Zaharia. His research interests lie broadly in the intersection of machine learning and systems. Currently, he is working on deep learning applied to video analysis.

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