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 with these essential tools.

To make machine learning (ML) more accessible, Amazon launched Amazon SageMaker Studio Lab at AWS re:Invent 2021. Today, tens of thousands of customers use it every day to learn and experiment with ML for free. We made it simple to get started with just an email address, without the need for installs, setups, credit cards, or an AWS account. SageMaker Studio Lab resonates with customers who want to learn in either an informal or formal setting, as indicated by a recent survey that suggests 49% of our current customer base is learning on their own, whereas 21% is taking a formal ML class. Higher learning institutions have started to adopt it, because it helps them teach ML fundamentals beyond the notebook, like environment and resource management, which are critical areas for successful ML projects.