Posted On: Oct 10, 2022
Amazon SageMaker comes with two options to spin up fully managed notebooks for exploring data and building machine learning (ML) models. The first option is fast start, collaborative notebooks accessible within Amazon SageMaker Studio - a fully integrated development environment (IDE) for machine learning. You can quickly launch notebooks in Studio, easily dial up or down the underlying compute resources without interrupting your work, and even share your notebook as a link in few simple clicks. In addition to creating notebooks, you can perform all the ML development steps to build, train, debug, track, deploy, and monitor your models in a single pane of glass in Studio. The second option is Amazon SageMaker Notebook Instance - a single, fully managed ML compute instance running notebooks in cloud, offering customers more control on their notebook configurations. Today, we are excited to announce that both SageMaker Studio and SageMaker Notebook Instance now come with JupyterLab 3 notebooks to boost productivity of data scientists and developers building ML models on SageMaker.
With this update, you now have access to a modern interactive development environment (IDE) complete with developer tools for code authoring, refactoring and debugging, and support for latest open source JupyterLab extensions. With the integrated debugger you can inspect variables and step through breakpoints while you interactively build your data science and machine learning (ML) code. In addition, using the Language Server extension, you can enable modern IDE functionality such as tab-completion, syntax highlighting, jump to reference, and variable renaming across notebooks and modules, making you much more productive. You can read more about this launch in this blog post.