Exciting new GitHub features powering machine learning

Machine learning enthusiast Seth Juarez shares his excitement about recent GitHub Universe announcements that enhance the integration of machine learning projects on GitHub, particularly through improvements in Jupyter Notebooks and the use of GitHub Codespaces. Juarez discusses the benefits of using Jupyter Notebooks for the exploration phase of machine learning, despite challenges in collaboration and remote access. He highlights the enhanced rendering capabilities and NbDime's side-by-side code difference display within GitHub. The introduction of GPUs for Codespaces and zero-configuration notebooks are praised for enabling effective remote execution and model building, with Juarez successfully running a PyTorch time-series analysis project in a browser. He underscores the importance of transitioning exploratory code to scripts for MLOps practices and appreciates the new debugging capabilities for model building using VS Code. Juarez also notes the support for JupyterLab, allowing users to maintain their preferred ML workflows, and invites feedback on further improvements for integrating machine learning with GitHub.