gidim

Excited to see more people building in this space. From what we've seen with customers it's critical to be able to compare what you're seeing in production to what you trained on (rather than historical period). That's almost the textbook definition of drift. Do you have a sense on how to approach that?

At Comet.com (disclaimer: i'm the CEO/Co-founder) we provide experiment tracking and artifacts management so we have the training distributions for comparison. I'm always curious how it looks like for a monitoring only solution

Streamlit is awesome but solves a different use case. There's definitely similarities but also a few core differences. 1. Comet Panels reads the experimentation data from our API/Database. Streamlit is much more like Jupyter notebooks where you'd have to setup your own data store and implement logic to update it with new experiment data. This also means that if you pull a visualization from the gallery it should work out of the box as the API is standard among everyone! 2. Comet Panels is freely hosted so no need to setup a webserver 3. Comet Panels is natively written in JS/HTML/CSS so you can use any JS library out of the box. Streamlit is written in Python and you might be limited to the built in UI components (or you can build a custom on which is also in JS/HTML/CSS)