[ATOM] Advanced/All Topics on Machine Learning discussion group

This month's discussion is led by Thomas Liu:

Many of us practically live in Jupyter notebooks to work on data science and machine learning problems, yet we also view them as the antithesis of production-ready. As a result, organizations spend a lot of time re-engineering notebook code into packages, pipelines, and services. Where do the problems contributing to this notoriety of notebooks stem from? Are notebooks in production so far-fetched of an idea?

Join us for a presentation, demo, and open forum on ways we can bridge the gap between notebooks and production, encompassing coding habits, collaboration-enabling notebook extensions such as papermill and jupytext, and scenarios where notebooks in production may surprisingly be a perfect match.

Would you like to lead a discussion? Reach out to us on Slack: @hanna, @stuart, @madrury.

About ATOM:
Advanced/All Topics on Machine learning ( ATOM ) is a learning and discussion group for machine learning and statistical modeling. We focus on how these ideas are applied to solve real-world problems, so our discussions cover algorithms, implementation, interpretation, rigor, and ethics. We assume some level of familiarity with machine learning, but otherwise, attempt to cover multiple dimensions of what it means to work in machine learning.

As a discussion group, we strongly encourage participation, so be sure to read up about the topic of conversation beforehand!

ATOM can be found on PuPPy’s Slack under the channel #atom, and on PuPPy’s Meetup.com events. Info for the slack channel and PuPPy, in general, can be found at https://www.pspython.com/.

Slides from previous discussions can be found at https://gitlab.com/users/puppy-atom/projects.