AWS launched managed Ray capabilities on SageMaker HyperPod with Amazon EKS, enabling teams to easily create and monitor Ray clusters and run resilient distributed training and accelerated inference, reducing infrastructure costs and speeding up model development in the region.

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AWS SageMaker HyperPod Adds Managed Ray on EKS for Distributed Training

Overview

FAQ

What is Ray on SageMaker HyperPod?

Ray is an open-source distributed computing framework, now fully managed on SageMaker HyperPod via EKS, enabling flexible model training and inference.

How does this compare to previous solutions?

Previously, teams had to manage Ray manually on EKS; now AWS provides centralized management, built-in observability, and SageMaker Studio integration, reducing operational overhead.

Should MENA teams adopt this now?

Yes, especially for organizations working on large language models or high-throughput inference applications, as it lowers costs and accelerates development cycles.

Source: AWS Machine Learning

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