Quick Start
Access fully managed Jupyter Notebooks in Studio quickly. SageMaker Studio notebooks come pre-configured with deep learning environments for AWS-optimized TensorFlow and PyTorch to help you get started with model building.

Familiar Jupyter Notebooks in the cloud
Use the Jupyter and JupyterLab Notebooks that you know and trust 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 ML, while keeping your compute environment secure and up to date with latest open-source software.

Prepare data at scale
Simplify your data workflows with a unified notebook environment for data engineering, analytics, and ML. Create, browse, and connect to Amazon EMR clusters and AWS Glue Interactive Sessions directly from SageMaker Studio notebooks. Monitor and debug Spark jobs using familiar tools such as Spark UI right from the notebooks. Use the built-in data preparation capability powered by Amazon SageMaker Data Wrangler directly from the notebooks to visualize data, identify data quality issues, and apply recommended solutions to improve data quality and model accuracy without writing a single line of code.

Get AWS performance and scale
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 by selecting from the broadest choice of compute-optimized and GPU-accelerated instances in the cloud. Use the built-in SageMaker Python SDK to train and deploy models on SageMaker. Get Jupyter logs in Amazon CloudWatch to track events and metrics, detect anamolous behavior, set alarms, and discover usage patterns.

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.

Get inspired
Looking for ideas on how to build? Your notebook instance contains more than 200 example notebooks provided by SageMaker, along with code that shows how to apply ML solutions using SageMaker.

Automatic conversion of notebook code to production-ready jobs
Once a notebook is selected, SageMaker Studio notebook takes a snapshot of the entire notebook, packages its dependencies in a container, builds the infrastructure, runs the notebook as an automated job on a schedule set by the practitioner, and deprovisions the infrastructure upon job completion–reducing the time it takes to move a notebook to production from weeks to hours. 

Built for teams
Set up your team access to SageMaker Studio notebooks using AWS IAM Identity Center (successor to AWS Single Sign-on). Create separate environments for platform administrators and business leaders to monitor cost and usage of SageMaker Studio. Create shared spaces where your teams can read, edit, and run notebooks together in real time to streamline collaboration and communication. Teammates can review results together to immediately understand how a model performs without passing information back and forth. With built-in support for services like BitBucket and AWS CodeCommit, teams can easily manage different notebook versions and compare changes over time.All resources are automatically tagged, making it easier to monitor costs and plan budgets using tools such as AWS Budgets and AWS Cost Explorer.

Customizable
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.