Amazon SageMaker Studio is a single web-based interface with comprehensive machine learning (ML) tools and a choice of fully managed integrated development environments (IDEs) to perform every step of ML development, from preparing data to building, training, deploying, and managing ML models. Today, we are excited to announce a new and faster fully managed JupyterLab offering, the latest web-based IDE for notebooks, code, and data.

You can now launch fully managed JupyterLab in seconds with pre-configured SageMaker Distribution, a pre-built docker image with mutually compatible popular ML libraries, including deep learning frameworks such as PyTorch, TensorFlow and Keras; popular python packages such as numpy, scikit-learn and pandas. You now have access to the latest fully-featured JupyterLab 4 version and generative AI-powered coding companions, such as Amazon Code Whisperer, to quickly author, debug, explain, and test code. You can scale your compute resources up or down with the broadest selection of compute and easily persist your packages across instance changes by quickly creating custom conda environments. Further, you can also bring your custom built images to power your environment with customized JupyterLab and ML libraries.

JupyterLab on Amazon SageMaker Studio is available in all Amazon Web Services (AWS) regions where Amazon SageMaker Studio is currently available, except China and the AWS GovCloud (US) regions. To learn more, please refer to the blog post and JupyterLab documentation.