Use machine learning environments offered by SageMaker
Amazon SageMaker supports the following machine learning environments.
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Amazon SageMaker Studio: Lets you build, train, debug, deploy, and monitor your machine learning models.
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Amazon SageMaker Notebook Instances: Lets you prepare and process data, and train and deploy machine learning models from a compute instance running the Jupyter Notebook application.
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Amazon SageMaker Studio Lab: Studio Lab is a free service that gives you access to AWS compute resources, in an environment based on open-source JupyterLab, without requiring an AWS account.
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Amazon SageMaker Canvas: Gives you the ability to use machine learning to generate predictions without needing to code.
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Amazon SageMaker geospatial: Gives you the ability to build, train, and deploy geospatial models.
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RStudio on Amazon SageMaker: RStudio is an IDE for R
, with a console, syntax-highlighting editor that supports direct code execution, and tools for plotting, history, debugging and workspace management. -
SageMaker HyperPod: SageMaker HyperPod lets you provision resilient clusters for running machine learning (ML) workloads and developing state-of-the-art models such as large language models (LLMs), diffusion models, and foundation models (FMs).
To use these machine learning environments, except Studio Lab, SageMaker Notebook Instances, and SageMaker HyperPod, you or your organization's administrator must create an Amazon SageMaker Domain. Studio Lab has a separate onboarding process.
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