This post is co-written with Stephen Aylward, Matt McCormick, Brianna Major from Kitware and Justin Kirby from the Frederick National Laboratory for Cancer Research (FNLCR).
Amazon SageMaker Studio Lab provides no-cost access to a machine learning (ML) development environment to everyone with an email address. Like the fully featured Amazon SageMaker Studio, Studio Lab allows you to customize your own Conda environment and create CPU- and GPU-scalable JupyterLab version 3 notebooks, with easy access to the latest data science productivity tools and open-source libraries. Moreover, Studio Lab free accounts include a minimum of 15 GB of persistent storage, enabling you to continuously maintain and expend your projects across multiple sessions and allowing you to instantly pick up where your left off and even share your ongoing work and work environments with others.
A key issue faced by the medical image community is how to enable researchers to experiment and explore

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