How GumGum serves its CV at scale
I had the pleasure of speaking with Greg Chu today at the Los Angeles Computer Vision Meetup hosted at GumGum HQ in Santa Monica. We had a great turn-out and I really enjoyed explaining how GumGum auto-scales our CV ML inference systems.
Tech talk overview
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).
You can view our Meetup slides here