ToolsAugust 24, 2026via AWS Machine Learning Blog
Introducing new Ray capabilities on SageMaker HyperPod
Why it matters
SageMaker HyperPod's new Ray integration lets ML engineers run distributed training and inference on Kubernetes without managing Ray infrastructure separately. This consolidates the dev experience for practitioners building multi-node workloads on AWS.
Key signals
- AWS SageMaker HyperPod adds managed Ray support on Amazon EKS
- Ray clusters can be created and monitored from SageMaker Studio
- JupyterLab and Code Editor notebooks can connect to live Ray clusters
- Built on open-source KubeRay and standard Ray APIs
- Includes out-of-the-box observability for distributed training and inference
- Resilient distributed training capability
- SageMaker HyperPod adds managed Ray support on Amazon EKS
- Built-in observability for Ray clusters
- Supports resilient distributed training and accelerated inference
- Uses open-source KubeRay and standard Ray APIs
- Launches from SageMaker Studio
The hook
Amazon brings managed Ray to SageMaker HyperPod—native Kubernetes support for distributed training without the ops overhead.
Amazon SageMaker HyperPod now offers managed Ray support on Amazon EKS. Create and monitor Ray clusters, connect JupyterLab and Code Editor notebooks to live clusters, get out-of-the-box observability, and run resilient distributed training and accelerated inference from SageMaker Studio, all with o…