Presented By O’Reilly and Cloudera
Make Data Work
21–22 May 2018: Training
22–24 May 2018: Tutorials & Conference
London, UK

Real-time deep learning on video streams

eran avidan (Intel)
14:05–14:45 Wednesday, 23 May 2018
Data science and machine learning, Streaming systems and real-time applications
 Location: Capital Suite 13 Level: Intermediate
Secondary topics:  Security and Privacy
Average rating: ****.
(4.50, 2 ratings)
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Who is this presentation for?

  • System architects, software developers, and data engineers

Prerequisite knowledge

  • A basic understanding of machine learning, deep learning, containers and microservices, databases, and programming

What you'll learn

  • Explore an architecture that supports real-time video analysis with deep learning

Description

Deep learning has recently become an abundant technology for analyzing video data. However, the increasing resolution and frame rates of videos make real-time analysis a remarkably challenging task.

Eran Avidan offers an overview of a novel architecture based on Redis, Docker, and TensorFlow that enables real-time analysis of high-resolution streaming video. The solution can serve advanced deep learning algorithms at subsecond rates and appears fully synchronous to the user despite containing an asynchronous backend. Eran offers a demo using visual inspection and shares results that highlight the solution’s applicability to real-time neural network processing of videos. The approach is generalizable and can be applied to diverse domains that require video analytics.

Photo of eran avidan

eran avidan

Intel

Eran Avidan is a senior software engineer in Intel’s Advanced Analytics Department. Eran enjoys everything distributed, from Spark and Kafka to Kubernetes and TensorFlow. He holds an MS in computer science from the Hebrew University of Jerusalem.