Select the JupyterLab version to use for an Amazon SageMaker Studio instance.

Use Amazon SageMaker Assets to provide controlled and regulated access to assets , models or data tables, belonging to your organization. Within SageMaker Assets, users from different AWS accounts can create and share assets related to specific business problems without additional administrator overhead. Instead of having permissions being statically tied to their identity, users can provide permissions to assets that they’re using for their active workflows.

Learn how to use Jupyter AI in SageMaker.

RStudio on Amazon SageMaker is an integrated development environment for R with a console, syntax-highlighting editor that supports direct code execution, and tools for plotting, history, debugging and workspace management.

Learn how Code Editor, based on Code-OSS, Visual Studio Code - Open Source supports writing, testing, debugging, and running your analytics and machine learning code.

Modify your Reserved Instances and continue to benefit from your capacity reservation and billing benefit when your computing needs change.

Use Amazon EC2 for scalable computing capacity in the AWS Cloud so you can develop and deploy applications without hardware constraints.

Use Amazon EC2 for scalable computing capacity in the AWS Cloud so you can develop and deploy applications without hardware constraints.

Get started with Amazon EC2. Learn to launch, connect to, and use a Linux instance.

Use Amazon SageMaker built-in algorithms.