Posted On: Sep 24, 2021

Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). SageMaker Studio provides a single, web-based visual interface where you can perform all ML development steps required to prepare, build, train and tune, deploy and manage models. With a single click, data scientists and developers can quickly spin up SageMaker Studio Notebooks for exploring datasets and building models. Now you can use lifecycle configurations to automate customizations for your Studio development environment.

Lifecycle configurations are shell scripts triggered by SageMaker Studio lifecycle events, for example, starting a new Studio notebook. You can use the scripts to customize Studio, for example, install custom packages, configure notebook extensions, preload datasets, and set up source code repositories. Lifecycle configurations in conjunction with the capability to bring your own container image to SageMaker Studio gives you complete flexibility and control to configure Studio to meet your specific needs. For example, you can create a minimal set of base container images with the most commonly used packages and libraries, and then use lifecycle configurations to install additional packages for specific use cases across your data science and ML teams.

The feature is now available in both Amazon Web Services China (Beijing) Region, operated by Sinnet, and Amazon Web Services China (Ningxia) Region, operated by NWCD. You can create lifecycle configurations and attach them to your Studio domain or to an individual user using Amazon CLI and Amazon SDK. To learn more about SageMaker Studio visit the SageMaker user guide.