Karmada in Action - Scaling AI Workloads Across Multi-Cluster at Scale

Explore how to efficiently scale AI workloads across multiple Kubernetes clusters using Karmada, an open-source multi-cluster orchestration solution. Learn why multi-cluster deployment is essential for AI applications and discover the key challenges organizations face when managing AI workloads at scale. Understand Karmada's core capabilities including multicluster scheduling, resource interpretation, federated resource quotas, multi-cluster queuing, and federated horizontal pod autoscaling. Examine real-world practices from industry experts at Huawei and Bloomberg who demonstrate practical strategies for implementing Karmada in production environments. Gain insights into the technical architecture and decision-making processes that enable organizations to optimize resource utilization, improve fault tolerance, and achieve better performance for AI applications across distributed infrastructure. Discover how Karmada addresses the complexities of cross-cluster resource management and learn actionable approaches for implementing multi-cluster AI workload orchestration in your own projects.