Developers often use code coverage as a target, which makes it a bad measure of test quality.

Mutation testing changes the game: create mutant versions of your code that break your tests, and you'll quickly start to write better tests!

Come and learn to use it as part of your CI/CD process. I promise, you'll never look at penguins the same way again!

Working with python is fun. Managing python packaging, linters, tests, CI, etc. is not as fun.

Every maintainer needs to worry about consistent styling, quality, speed of tests, etc as the project grows.

Monorepos have been successful in other communities - how does it work in Python ?

I will present the challenges we encountered while migrating an ML model from batch to real-time predictions and how we handled them. In particular, I will focus on the design decisions and open-source tools we built to test the code, data and models as part of the CI/CD pipeline and enable us to ship fast with confidence.

We will delve into the importance of effective data visualisation in today's world. We will explore how it can help convey insights from data using Matplotlib and best practices for creating informative visualisations. We will also discuss the limitations of static visualisations and examine the role of continuous integration in streamlining the process and avoiding common pitfalls. By the end of this talk, you will have gained valuable insights and techniques for creating informative and accurate data visualisations, no matter what tools you're using.