Using Kubernetes to Speed Development and Deployment of Edge Computer Vision Applications

This session will present real-world deployments of computer vision solutions using Kubernetes and show how containerization enables continuous AI updates on a massive scale at the edge. Housing the code, dependencies and environment in one logical, portable block, container enable AI applications to run across different platforms, toolsets and chipsets. This speeds deployments of new applications by 10x or more, and makes the development, packaging and deployment process predictable and consistent. Attendees will hear how a Kubernetes-driven video processing pipeline trained new models for a multinational fuel dispenser manufacturer in days using existing source data, then deployed by adding a new container to a cluster at the edge. These new models enabled the company to detect physical problems like gas nozzles left in vehicles and gauge the effectiveness of on-site dispenser marketing initiatives within weeks of the capabilities request.