Accelerate Python with Julia
Stephan Sahm

You want to accelerate your Python code, but going C is too tedious? Julia is a fresh alternative which flows like Python and runs like C. Join this tutorial to learn how to use Julia to easily speed up Python.

Cloud Infrastructure From Python Code: How Far Could We Go?
Etzik Bega, Asher Sterkin

Why Infrastructure as Code is not enough and what needs to be done to make Python trully cloud-native programming language?

How to increase diversity in open source communities
Maren Westermann

Learn about strategies for increasing diversity in #opensource projects presented by @MarenWestermann

PyLadies Panel Session. Tech Illusions and the Unbalanced Society: Finding Solutions for a Better Future

PyLadies chapters around the world reflect on their contributions in advocating for gender representation and leadership as well as combating biases and the gender pay gap.

PyLadies Workshop

Know your rights! PyLadies and Berlin Tech Workers Coalition will unveil important details on work contracts and your rights to get you covered in case of layoffs

Rethinking codes of conduct
Tereza Iofciu

Did you know that the Python Software Foundation Code of Conduct is turning 10 years old in 2023? It was voted in as they felt they were “unbalanced and not seeing the true spectrum of the greater community”. Why is that a big thing? Come to my talk and find out!

The CPU in your browser: WebAssembly demystified
Antonio Cuni

WebAssembly is essentially a virtual and efficient CPU embedded in your browser. Let's see what it is!

Thou Shall Judge But With Fairness: Methods to Ensure an Unbiased Model
Nandana Sreeraj

Biased models can impact each of us. While it may feel abstract, AI fairness can be achieved through many methods and metrics. More so, mitigation reports can initiate you to responsible AI. Check out my talk & demo at PyData Berlin.

What could possibly go wrong? - An incomplete guide on how to prevent, detect & mitigate biases in data products
Lea Petters

Data Ethics: What could possibly go wrong? - An incomplete guide on how to prevent, detect & mitigate biases in data products

Workshop on Privilege and Ethics in Data
Tereza Iofciu, Paula Gonzalez Avalos

Data-driven Products are built by humans. Humans are intrinsically biased. This bias goes into the products, which amplifies the original bias. In this tutorial, you will learn how to identify your biases and reflect on the consequences of unchecked biases in Data Products.