Speaking Appearances

Ethan Speaking
2025-05-06
University of Cincinnati Analytics Summit 2025

Moving Faster, Without Breaking Things

Investing in tools that are speedups, not speed bumps
Small, early-stage technical organizations focus almost exclusively on their product. Then as headcount grows, smart companies invest more internally, especially in developing internal tools. But without a clear vision and strategy, these investments can become confusing, buggy, and poorly-documented, ultimately creating new hurdles for developers and data scientists. Getting the efficiency gains without falling into these traps is hard, but more than worth the effort. Drawing from my experience building internal tools at various companies – from a growth-stage startup to a very mature bank – I discussed the different strategies I’ve seen succeed and fail.
conference listing slides
2023-11-19
University of Cincinnati Data Science Symposium 2023

Programming with AI

How LLMs are changing the way we write code
Large language models were the biggest story in tech in 2023. Many industries are grappling with whether and how LLMs will impact their work in the future, but in the case of data science and software development, useful tools already exist and some practitioners are incorporating them into workflows to great effect. In this talk, I discussed how LLMs can amplify the practice of writing code in the present, and how the industry might change in response over the coming years.
conference listing slides video
2023-04-20
PyCon 2023

Deploying a Model Prediction Server (Tutorial)

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.
conference listing slides youtube
2022-11-08
University of Cincinnati Data Science Symposium 2022

ML Engineering

From models to value
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.
conference listing slides
2021-10-30
PyData Global 2021

Foundational Infrastructure to Create a Successful Data Science Team

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.
conference listing slides youtube
2021-09-29
Python Bytes Podcast

Python Bytes, Episode 252

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.
podcast
2019-10-29
Talk Python Podcast

Scaling Data Science Across Python and R

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˚.
podcast