On May 28, 2019, myself (Greg Chu) and Corey Gale presented a talk titled “How GumGum Serves its CV at Scale” at the LA Computer Vision Meetup in GumGum’s Santa Monica office.
Given the rapidly growing utility of computer vision applications, how do we deploy these services in high-traffic production environments to generate business value? Here we present GumGum’s approach to building infrastructure for serving computer vision models in the cloud. We’ll also demo code for building a car make-model detection server.
- Multivitamin: an open-sourced Python framework for serving library-agnostic machine learning models
- Containerization: packaging everything you need into a single portable artifact
- CI/CD: automating builds and releases with Drone CI
- Custom auto-scaling: using AWS Lambda to scale our infrastructure based on business metrics
Greg Chu is a Senior Computer Vision Scientist at GumGum, where he works on both the training and large-scale deployment of object detection and recognition models. These models are applied within GumGum’s products for contextual advertising and sports sponsorship analytics. Greg has a background in biomedical physics. In his Ph.D research he developed tumor segmentation models to assess the clinical progression of patients in FDA clinical drug trials.
Corey Gale is a Senior DevOps Engineer at GumGum. He works on automating cloud infrastructure for highly-scalable systems using open-source technologies. With his background in Robotics Engineering, Corey is a believer that through automation, anything is possible. He is also obsessed with process (measure all the things!), cost-reduction and entrepreneurship (Corey actually created a food delivery app in 2012, well before they became mainstream).