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AWS Machine LearningManage Amazon SageMaker HyperPod Spaces directly from SageMaker Studio
· 1 min read · Summary from AWS Machine Learning
Data scientists and ML engineers can now create, configure, start, stop, and open Amazon SageMaker Spaces on SageMaker HyperPod EKS clusters directly from SageMaker Studio. Launch JupyterLab and Code Editor environments in a few clicks, without using command-line tools.
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Amazon SageMaker now lets users manage HyperPod Spaces directly from SageMaker Studio. They can create, configure, start, stop, and open Spaces with a few clicks, launching JupyterLab and Code Editor environments without command‑line tools.
Small business owners who use machine learning can save time and reduce complexity by managing resources in a single interface. It lowers the barrier to entry for experimenting with ML models and speeds up deployment, helping businesses stay competitive.
Try opening a SageMaker Space from Studio this week to see how quickly you can launch a JupyterLab environment. Watch for updates on how this integration can streamline your data science workflows.