Jupyter notebooks allow data scientists to create, share, and streamline work for data visualization from raw code to fully functional machine learning (ML) models. With Amazon SageMaker Studio, you can spin up Jupyter notebooks quickly without the need to manage the underlying compute resources.
You can easily dial the required compute scale up or down, and the changes happen automatically in the background. The notebooks within SageMaker Studio are shareable, enabling collaboration with increased productivity.
In this session, learn how to use fully managed Jupyter notebooks complemented by an in-depth demonstration on how to build ML models at scale.