SageMaker Studio
An integrated machine learning development environment from Amazon AWS offering a unified visual interface that supports the full workflow of data preparation, model training, deployment, and monitoring, with built-in development tools including JupyterLab and Code Editor.
Timeline (last 90 days)
Users can launch JupyterLab and Code Editor environments on HyperPod clusters via SageMaker Studio without switching to command-line tools.
SageMaker Spaces is a type of isolated working environment unit within SageMaker Studio, where each Space has its own independent storage and compute resource configuration.
The feature supports full lifecycle operations within Studio, including creating configurations, launching and stopping environments, and opening JupyterLab or Code Editor with a single click.
All Facts (3)
Users can launch JupyterLab and Code Editor environments on HyperPod clusters via SageMaker Studio without switching to command-line tools.
50%UnverifiedSageMaker Spaces is a type of isolated working environment unit within SageMaker Studio, where each Space has its own independent storage and compute resource configuration.
50%UnverifiedThe feature supports full lifecycle operations within Studio, including creating configurations, launching and stopping environments, and opening JupyterLab or Code Editor with a single click.
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