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 data, as well as build, train, and deploy models. With a single click, data scientists and ML 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 lifecycle configurations feature is now available in all AWS regions where SageMaker Studio is available. You can create lifecycle configurations and attach them to your Studio domain or to an individual user using AWS CLI and AWS SDK. You can quickly get started using our sample scripts and examples. To learn more about this new capability visit the SageMaker Studio user guide.