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AWS puts JupyterLab and VS Code inside EKS clusters — 5 minutes to a configured Space vs 3–5 days of platform work
AWS Machine Learning Blogmedium signalThe SageMaker AI Spaces add-on for Amazon EKS runs managed JupyterLab and Code Editor environments on the same cluster hosting training pipelines, so notebooks keep GPU nodes, shared storage and IAM identity instead of living in a separate environment. AWS claims standing up a standalone JupyterHub with GPU access, storage and auth typically takes a platform team 3–5 days versus about 5 minutes for a data scientist to launch a Space. Consolidating interactive and training workloads is claimed to lift GPU utilization by up to 30%; the SSM advanced-instances tier costs about $0.00695/hr per Space pod. Requires EKS v1.30+.
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