Engineering Considerations for Running Machine Learning Models at the Edge: Application in Body Scanning for eCommerce

This talk will review the engineering considerations to support the operation of body scanning machine learning models to evaluate the usability of images for generating a body double in the Amazon shopping application. In order to create next generation shopping experiences which reflect a customer’s body proportions based on their photos, the engineering team needed a low latency way to validate if the data would be able to produce a highly accurate result. The body estimation models were used to generate a 3D simulation of a custom shirt draped on the person using the iOS or Android shopping app. Engineering requirements for operating this model at the edge included latency of application download, model run time, model size, and the ability to instrument the experience for defection detection while keeping the customer data secure.


Jenn Lin

Principal Engineer and Sr. Software Development Manager @Amazon

Jenn Lin is currently a Principal Engineer and Sr. Software Development Manager leading a team for Amazon's Selling Partner Services team that focuses on automated profitability for the Amazon business. Prior to this, Jen was a Principal Engineer for Amazon Fashion and building the next generation of clothes shopping experiences.

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Herak Sen

Principal Software Engineer @Amazon

Herak Sen is currently a Principal Engineer for Amazon Fashion where he focusses on helping customer find the perfect fit. He is responsible for building next generation of customer shopping experiences and garment manufacturing technologies.

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Tuesday Oct 25 / 11:50AM PDT ( 50 minutes )




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