If we want to run data science workloads (e.g. using Tensorflow, PyTorch, and others) in containers (for local development or production on Kubernetes), we need to build container images. Doing that with a Dockerfile is fairly straightforward, but is it the best method? In this talk, we'll take a well-known speech-to-text model (Whisper) and show various ways to run it in containers, comparing the outcomes in terms of image size and build time.

Ever tried passing a Polars Dataframe to a data science library and found that it...just works? No errors, no panics, no noticeable overhead, just...results? This is becoming increasingly common in 2025, yet only 2 years ago, it was mostly unheard of. So, what changed? A large part of the answer is: Narwhals.

Narwhals is a lightweight compatibility layer between dataframe libraries which lets your code work seamlessly across Polars, pandas, PySpark, DuckDB, and more! And it's not just a theoretical possibility: with ~30 million monthly downloads and set as a required dependency of Altair, Bokeh, Marimo, Plotly, Shiny, and more, it's clear that it's reshaping the data science landscape. By the end of the talk, you'll understand why writing generic dataframe code was such a headache (and why it isn't anymore), how Narwhals works and how its community operates, and how you can use it in your projects today. The talk will be technical yet accessible and light-hearted.

From: Chrono-Regulatory Commission, Temporal Enforcement Division To: PyData Berlin Organising Committee Subject: Citation #TMP-2077-091 - Unauthorised Spacetime Disturbance

Dear Committee, Our temporal monitoring systems have detected an unauthorised chronological anomaly emanating from your facility (Berliner Congress Center, coordinates 52.52068°N, 13.416451°E) scheduled to manifest on September 1st at 9:20 a.m.