What is SageMaker notebooks?
Launch fully managed JupyterLab from Amazon SageMaker Studio in seconds. Use the integrated development environment (IDE) for notebooks, code, and data. You can use the quick start, collaborative notebooks in the IDE to access purpose-built ML tools in SageMaker and other AWS services for your complete ML development, from preparing data at petabyte scale using Spark on Amazon EMR, to training and debugging models, deploying and monitoring models and managing pipelines – all in one web-based visual interface. Easily dial compute resources up or down without interrupting your work.
Benefits of SageMaker notebooks
Build ML at scale
Quick start
Launch fully managed JupyterLab in Studio in seconds. SageMaker Studio comes pre-configured with pre-built SageMaker distribution containing popular packaging for ML, including deep learning frameworks like PyTorch, TensorFlow, and Keras; popular Python packages like NumPy, scikit-learn, and panda to help you get started with model building.
Elastic compute
Scale your underlying compute resources up or down, and use shared persistent storage to switch compute, all without interrupting your work. Pick from the broadest selection of compute resources offered by AWS, including the most powerful GPU instances for ML.