Introducing new Ray capabilities on SageMaker HyperPod

📰 AWS Machine Learning

Learn to leverage Ray on SageMaker HyperPod for managed distributed training and inference with Amazon EKS

intermediate Published 24 Aug 2026

Action Steps

Create a Ray cluster on Amazon EKS using SageMaker HyperPod
Connect JupyterLab and Code Editor notebooks to live Ray clusters
Configure out-of-the-box observability for monitoring
Run distributed training and accelerated inference from SageMaker Studio
Use open-source KubeRay and standard Ray APIs for customization

Who Needs to Know This

Machine learning engineers and data scientists can benefit from this integration to streamline their workflow and improve model performance

Key Insight

💡 SageMaker HyperPod now offers managed Ray support for seamless distributed training and inference

Full Article

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 open-source KubeRay and standard Ray APIs.

Read full article → ☆ Save to playlist ← Back to Reads