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Embedded Vision Insights: August 21, 2018 Edition

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Introduction to Creating a Vision Solution in the CloudGumGum
A growing number of applications utilize cloud computing for execution of computer vision algorithms. In this presentation, Nishita Sant, Computer Vision Scientist at GumGum, introduces the basics of creating a cloud-based vision service, based on GumGum's experience developing and deploying a computer vision-based service for enterprises. Sant explores the architecture of a cloud-based computer vision solution in three parts: an API, computer vision modules (housing both algorithm and server), and computer vision features (complex pipelines built with modules). Execution in the cloud requires the API to handle a continuous, but unpredictable, stream of data from multiple sources and task the appropriate computer vision modules with work. These modules consist of an AWS Simple Queue Service and an EC2 auto-scaling group and are built to handle both images and video. Sant discusses in detail how these modules optimally utilize instance hardware for executing a computer vision algorithm. Further, she discusses GumGum's method of inter-process communication, which allows for the creation of complex computer vision pipelines that require several modules to be linked. Sant also addresses cost and run-time tradeoffs between GPU and CPU instances.

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