Using Training-Operator to Schedule Distributed Edge-Cloud Collaborative AI Applications

This conference talk explores how to integrate KubeFlow training-operator into KubeEdge's Sedna framework to enable distributed edge-cloud collaborative AI applications. Learn how Edge AI can leverage local data processing for millisecond-level response times while evolving into edge-cloud collaborative systems. Discover how the integration extends distributed training capabilities to the edge, allowing for dynamic task allocation across cloud and edge environments through training-operator's group scheduling. Understand how this approach optimizes resource utilization and enhances edge-cloud AI efficiency, addressing the management and scheduling challenges of distributed AI applications. The presentation demonstrates practical methods for seamlessly deploying existing AI applications to edge environments while meeting diverse demands for real-time performance, accuracy, and privacy.