Access to fully managed Jupyter Notebooks in Studio in one click. SageMaker Studio Notebooks come pre-configured with deep learning environments for AWS-optimized TensorFlow and PyTorch to help you quickly get started with model building. You can easily dial up or down the underlying compute resources without interrupting your work.

Simplify your data workflows with a single unified notebook environment for data engineering, analytics and machine learning. Browse and query data sources, explore metadata and schemas, and start data processing jobs for analytics or ML workflows - without leaving the Studio Notebook. Interactively prepare data at scale using the built-in integration with Spark, Hive, and Presto running on Amazon EMR clusters and data lakes running on Amazon S3.

Scale your underlying compute resources up or down with a simple click. Shared persistent storage enables you to switch compute without interrupting your work. Pick from the broadest choice of compute resources offered by AWS, including the most powerful GPU instances for machine learning. Continue to browse and share notebooks even after shutting down instances.

Set up your team access to Studio Notebooks with a single click using AWS Single Sign-on (SSO). You can also share notebooks with a few clicks. They will get the exact same notebook, saved in the same place.

Bring your own notebook development environment to SageMaker Studio using a custom docker image. Use Lifecycle Configurations to automate and customize notebook environments for your team. For example, you can install custom packages and notebook extensions, preload datasets, and automate shutting down idle instances.

Use the Jupyter and JupyterLab Notebooks that you know and love in the fully managed SageMaker service. Forget the hassle of setting up compute resources, upgrading data science and ML packages, and applying security patches. SageMaker Notebook Instances let you focus entirely on doing ML, while keeping your compute environment secure and up to date with latest open source software.

Reduce the time it takes from data to insights with prepackaged data science and ML frameworks that are optimized for performance by AWS. Scale your resources easily by selecting from the broadest choice of compute-optimized and GPU-accelerated instances in the cloud. Use the built-in SageMaker Python SDK to easily train and deploy models on SageMaker. Get Jupyter logs in CloudWatch, enabling you to track events and metrics, detect anamolous behavior, set alarms and discover usage patterns.

Seamlessly integrate with your existing ML set up. Your teams can easily collaborate and share their notebooks by linking Git-based code repositories with their notebook instances, write lifecycle configuration scripts to automate the set up of their development environment, use SageMaker SDK Local Mode to quickly train and experiment with their models locally on the instances.

Looking for ideas on how to build? Your Notebook Instance contains more than 200 example notebooks provided by SageMaker, and contain code that shows how to apply machine learning solutions using SageMaker.