2025-09-03 –, B07-B08
Kubeflow is a platform for building and deploying portable and scalable machine learning (ML) workflows using containers on Kubernetes-based systems.
We will code together a simple Kubeflow pipeline, show how to test it locally. As a bonus, we will explore one solution to avoid dependency hell using the modern dependency management tool uv.
In this demo, you will learn how to set up and run locally a Kubeflow pipeline that:
- adheres standard pyproject.toml format
- keeps consistent python version and dependencies across components
- manages dependencies of all components at once, including lockfile
We will discuss how and why this enhanced setup can improve pipeline and dependency maintainability for systems running in production, while still taking advantage of the Kubeflow API flexibility and features.
None
Prerequisites:None
Abstract as a tweet (X) or toot (Mastodon):Kubeflow meets uv
I’m Fabrizio 🧉, PhD in Computational Neuroscience 🎓, now a Data Scientist 💻 in Hamburg (Germany). I work on fraud detection 🕵🏽 using neural networks and large datasets at one of the top e-commerce platforms in Europe.
As an open source advocate, I have contributed to a few projects and created a couple of Python packages that I maintain 🤓. Beyond that, I am generally interested in topics around 🐍 Python, 🤖 Machine Learning, 📊 Data Science, Computational modeling, 🚀 Scientific computing and the application of data-driven solutions to make people’s life better.