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.

AWS has introduced SageMaker Studio Lab, a free service to help developers learn machine-learning techniques and experiment with the technology. SageMaker Studio Lab provides users with all of the basics to get started, including a JupyterLab IDE, model training on CPUs and GPUs and 15 GB of persistent storage. SageMaker Studio Lab has all the basics to create data analytics, scientific computing, and machine-learning projects with notebooks, which can be easily imported and exported via the Git repo or a private Amazon S3 bucket. SageMaker Studio Lab becomes an alternative to the popular Google Colab environment, providing free CPU/GPU access. Another enhancement for AWS SageMaker is a visual, no-code tool called SageMaker Canvas.

Amazon Web Services (AWS) this week announced a public preview of SageMaker Studio Lab. AWS also announced the Artificial Intelligence & Machine Learning Scholarship. The new US $10 million program will prepare underrepresented and underserved students globally for careers in Machine Learning. The announcements came during this week's AWS re:Invent event. AWS hopes SageMaker Studio Lab will attract developers, academics and data scientists to learn and experiment with Machine Learning (ML).

At its re:Invent conference, AWS today announced SageMaker Studio Lab, a free service to help developers learn machine learning techniques and experiment with the technology. Studio Lab provides users with all of the basics to get started, including a JupyterLab IDE, model training on CPUs and GPUs and 15 GB of persistent storage. In addition, Amazon also today launched the AWS AI & ML Scholarship Program. The company is committing $10 million oer year to this program, which it runs in collaboration with Intel and Udacity. "The two initiatives we are announcing today are designed to open up educational opportunities in machine learning to make it more widely accessible to anyone who is interested in the technology," said Swami Sivasubramanian, Vice President of Amazon Machine Learning at AWS. "Machine learning will be one of the most transformational technologies of this generation. If we are going to unlock the full potential of this technology to tackle some of the world's most challenging problems, we need the best minds entering the field from all backgrounds and walks of life. We want to inspire and excite a diverse future workforce through this new scholarship program and break down the cost barriers that prevent many from getting started with machine learning."