Amazon SageMaker Studio Lab (currently in preview), a free, no-configuration ML service
Introducing Amazon SageMaker Studio Lab is a free, no-configuration service that allows developers, academics, and data scientists to learn and experiment with machine learning.
Introducing Amazon SageMaker Studio Lab is a free, no-configuration service that allows developers, academics, and data scientists to learn and experiment with machine learning.
Using Amazon SageMaker Studio Lab customers will be able to focus on experimenting with the data science aspect of machine learning, without having to set up, or configure any infrastructure. Based on the the open source JupterLab web application, customers have a completely open environment that enables them to leverage any framework, such as Pytorch, TensorFlow, MxNet, or Hugging Face, and libraries such as SciKitLearn, NumPy, and Pandas. Studio Lab has auto-save capabilities that will automatically save the customers’ user sessions, so they can pick up where they left off on their next user session. Other benefits of SageMaker Studio Lab is its integration to Github enabling customers to open, view, edit, and run any notebook, as well as integration to Git, an open source distributed version control system.
To get started vist here and request an account. It only takes a valid email address to register. After that you can quickly start learning and experimenting with Jupyter notebooks. We also make it easy to get started with assets like AWS Machine Learning University, Dive into DeepLearning, and Hugging Face notebooks.