Posted On: Oct 27, 2020
Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). With a single click, data scientists and developers can quickly spin up SageMaker Studio Notebooks to explore datasets and build models. Starting today, you can launch SageMaker Studio Notebooks with your own images.
SageMaker Studio Notebooks provide a set of built-in images for popular data science and ML frameworks and compute options to run notebooks. The built-in SageMaker images contain the Amazon SageMaker Python SDK and the latest version of the backend runtime process, also called kernel. Starting today, you can register custom built images and kernels, and make them available to all users sharing a SageMaker Studio domain. You can start by cloning and extending one of the example Docker files provided by SageMaker, or build your own images from scratch.
You can spin up notebooks using specific versions of popular ML frameworks such as Tensorflow, MxNet, PyTorch. You can use kernels other than IPython such as R, Julia and Scala. You can also customize the notebook environment with proprietary packages and libraries to run custom training script, or to enable access to your data lakes or on-premises data stores. The feature is now available in all AWS regions where Amazon SageMaker Studio is available. To get started, see the following list of resources: