Manage Edge AI Using ZEDEDA Kubernetes Service

In this Edge Field Day Showcase, ZEDEDA’s Distinguished Engineer, Hariharasubramanian C. S, discusses how ZEDEDA is tackling the growing importance and challenges of deploying AI at the edge. He highlights that factors like insufficient bandwidth, high latency, and data privacy concerns make it impractical to send all sensor data to the cloud for analysis. ZEDEDA’s solution is to bring AI to the edge, closer to the data source. This, however, introduces its own challenges, such as managing a wide range of hardware, ensuring autonomy in disconnected environments, and updating AI models at scale. Hari argues that Kubernetes, with its lightweight nature and robust ecosystem, is the ideal solution for packaging and managing complex AI pipelines at the edge.
This presentation demonstrates how ZEDEDA’s Kubernetes service simplifies the deployment of an Edge AI solution for car classification. Using a Helm chart, he shows how to deploy a multi-component application, including an OpenVINO inference server, a model-pulling sidecar, and a demo client application. The demo showcases how the ZEDEDA platform provides a unified control plane for zero-touch provisioning and lifecycle management of these components, all while keeping models in a private, on-premise network without exposing them to the cloud. He concludes by demonstrating the application’s real-time inference capabilities and encouraging developers to leverage ZEDEDA’s open-source repositories to build their own edge AI solutions.