Explore how to enhance machine learning performance on heterogeneous AI accelerators using Ray on Kubernetes. Learn about the challenges of managing AI workloads across diverse hardware and software accelerators, and discover a unified framework that seamlessly integrates with mainstream ML graph compilers. Understand how transparent backend acceleration technologies can automatically boost ML performance without requiring code changes to existing AI applications. Gain insights into emerging trends in AI workload management from cloud to edge, and see how this approach addresses the fragmentation issues in current ML acceleration techniques.