PyCon 2021 Notes, Part 1
I attended PyCon US 2021 on May 14-15. I liked the sessions that presented new tools or best practices, which will be covered in two blogs. The first blog contains notes from the first day (May 14th) while the second blog covers talks from the next day (May 15th).
I focused on talks about data science topics, with a few other talks that piqued my interest. For each talk, I’ve included short notes about the talk and links to associated resources. Hopefully, you will find these notes useful!
PyCon US is the annual national conference for Python in the United States. It includes workshops, coding sprints, talks, an expo, and more. Pre-COVID, PyCon was an in-person conference. For the second year, PyCon was held online. The registration fee was much lower than an in-person conference at $50 to $150. The proceeds benefit the Python Software Foundation, which funds Python core development, outreach, and other activities. If you have not already registered, I assume you can still sign up. Recordings of all the conference talks are available to registered attendees on the conference website.
Rubicon is an ML experiment tracking system developed by a Data Science team at Capital One. There are many experiment tracking systems these days — see, for example, the list of similar projects we are tracking at DataHut.ai . Rubicon seems fairly straightforward and non-invasive. It includes both experiment tracking and a nice visualization dashboard of experimental results. From this talk, I also learned about FSSPEC , an effort to create a common filesystem API with many backends. Rubicon leverages it to provide both local and Amazon S3 storage of experiments.
