Amazon SageMaker Notebooks

Fully managed notebooks for exploring data and building ML models

Quick start your ML development with fully managed Jupyter Notebooks in the cloud.

Scale your compute resources up or down with the broadest selection of compute-optimized and GPU-accelerated instances in the cloud.

Efficiently collaborate with teams across all steps of your ML lifecycle by editing the same notebooks together.

Go from data to insights up to 2X faster with optimizations for popular frameworks and packages such as Spark, NumPy and Scikit-learn.

Amazon SageMaker offers two types of fully managed Jupyter Notebooks for data exploration and building ML models: Amazon SageMaker Studio notebooks and Amazon SageMaker notebook instances.

SageMaker Studio notebooks

Quick start, collaborative notebooks that integrate with 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, tracking experiments, deploying and monitoring models and managing pipelines – all in Amazon SageMaker Studio – a fully integrated development environment (IDE) for ML. Easily dial compute resources up or down without interrupting your work. Share notebooks easily with your team using a sharable link or even coedit a single notebook at the same time.

Get started with SageMaker Studio notebook

Build ML models at scale with Amazon SageMaker Studio Notebooks (1:17)

Amazon SageMaker notebook instances

Standalone, fully managed Jupyter Notebook instances in the Amazon SageMaker console. Choose from the broadest selection of compute resources available in the cloud, including GPUs for accelerated computing, and work with the latest versions of open-source software that you trust.

Get started with SageMaker notebook instances

Introducing Next Generation SageMaker Notebooks (1:42)

How it works

  • SageMaker Studio Notebooks
  • How Amazon SageMaker Studio Notebook works
  • SageMaker Notebook Instances
  • How Amazon SageMaker Notebooks works

Key features

SageMaker Studio notebooks

SageMaker notebook instances

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.

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.



With Amazon SageMaker Studio, AstraZeneca was able to rapidly deploy a solution to analyze large amounts of data, accelerating insights while reducing the manual workload of its data scientists—crucial to AstraZeneca’s mission of discovering and developing life-changing medicines for people around the world.

“Rather than creating many manual processes, we can automate most of the ML development process simply within Amazon SageMaker Studio.”

Cherry Cabading, Global Senior Enterprise Architect – AstraZeneca

Read the case study

“We’re excited that our Vanguard data scientists and data engineers can now collaborate in a single notebook for analytics and machine learning. Now that Amazon SageMaker Studio has built-in integrations with Spark, Hive, and Presto all running on Amazon EMR, our development teams can be more productive. This single development environment will allow our teams to focus on building, training, and deploying machine learning models.”

Doug Stewart, Senior Director of Data and Analytics – Vanguard

“We have been waiting for a feature to create and manage Amazon EMR clusters directly from Amazon SageMaker Studio so that our customers could run Spark, Hive, and Presto workflows directly from Amazon SageMaker Studio notebooks. We are excited that Amazon SageMaker has now natively built this capability to simplify management of Spark and machine learning jobs. This will help our customers’ data engineers and data scientists collaborate more effectively to perform interactive data analysis and develop machine learning pipelines with EMR-based data transformations."

Stepan Pushkarev, CEO – Provectus

Get started with a tutorial

Follow this step-by-step tutorial to deploy a model for inference using Amazon SageMaker.

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Amazon Pinpoint getting started tutorial
Try a self-paced workshop

In this hands-on lab, learn how to use Amazon SageMaker to build, train, and deploy an ML model.

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Start building in the console

Get started building with Amazon SageMaker in the AWS Management Console.

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