José from the Livebook team. I don't think I can make a pitch because I have limited Python/R experience to use as reference. My suggestion is for you to give

José from the Livebook team. I don't think I can make a pitch because I have limited Python/R experience to use as reference. My suggestion is for you to give it a try for a day or two and see what you think. I am pretty sure you will find weak spots and I would be very happy to hear any feedback you may have. You can find my email on my GitHub profile (same username). In general we have grown a lot since the Numerical Elixir effort started two years ago. Here are the main building blocks: * Nx (https://github.com/elixir-nx/nx/tree/main/nx#readme https://github.com/elixir-nx/nx/tree/main/nx#readme): equivalent to Numpy, deeply inspired by JAX. Runs on both CPU and GPU via Google XLA (also used by JAX/Tensorflow) and supports tensor serving out of the box * Axon (https://github.com/elixir-nx/axon https://github.com/elixir-nx/axon): Nx-powered neural networks * Bumblebee (https://github.com/elixir-nx/bumblebee https://github.com/elixir-nx/bumblebee): Equivalent to HuggingFace Transformers. We have implemented several models and that's what powers the Machine Learning integration in Livebook (see the announcement for more info: https://news.livebook.dev/announcing-bumblebee-gpt2-stable-diffusion-and-more-in-elixir-3Op73O https://news.livebook.dev/announcing-bumblebee-gpt2-stable-d...) * Explorer (https://github.com/elixir-nx/explorer https://github.com/elixir-nx/explorer): Series and DataFrames, as per this thread. * Scholar (https://github.com/elixir-nx/scholar https://github.com/elixir-nx/scholar): Nx-based traditional Machine Learning. This one is the most recent effort of them all. We are treading the same path as scikit-learn but quite early on. However, because we are built on Nx, everything is derivable, GPU-ready, distributable, etc. Regarding visualization, we have "smart cells" for VegaLite and MapLibre, similar to how we did "Data Transformations" in the video above. They help you get started with your visualizations and you can jump deep into the code if necessary. I hope this helps!