Amazon has introduced managed Ray support for SageMaker HyperPod running on Amazon EKS. The capability lets users create and monitor Ray clusters, connect JupyterLab and Code Editor notebooks to live clusters, and access observability features by default. According to AWS, the integration supports resilient distributed training and accelerated inference launched from SageMaker Studio, built on the open-source KubeRay project and standard Ray APIs.
Why it matters
By offering Ray as a managed service on EKS, AWS reduces the setup work involved in running distributed training and inference workloads, while keeping compatibility with open-source KubeRay and standard Ray APIs.
Who should care
Machine learning engineers and teams using SageMaker HyperPod who need distributed training or accelerated inference and prefer working with Ray from notebooks and SageMaker Studio.