This is a 3-hour tutorial during which we start with a trained scikit-learn model and incrementally build a working FastAPI application to deliver its predictions in realtime. It’s targeted at data scientists, and no prior experience with API development is expected.
University of Cincinnati Data Science Symposium 2022
This was one of the featured presentations at UC’s Data Science Symposium. I discussed what machine learning engineering is, why companies should consciously allocate roles for it, and how to organize a combined team of data scientists and ML engineers. Unfortunately the talk wasn’t recorded, but you can view the slides using the link below.
PyData Global 2021
My talk with teammates Brad Boehmke and Gus Powers about what it takes to build internal tooling that effectively enables a large department of data scientists. Fairly high-level and doesn’t require too much technical background.
Python Bytes Podcast
I was a guest on the Python Bytes Podcast to join in the week’s roundup of news in the Python world. We talked about JupyterLab Desktop, requests-cache, and a new PEP in the works to simplify the typing of decorators.
Talk Python Podcast
My boss, Brad Boehmke, and I went on the Talk Python Podcast to discuss the benefits and challenges of supporting both R and Python across a large data science department at 84.51˚.