Day 1 • 10:50 R2
What I Learned (and Broke) Migrating Large-Scale Production Data From SQL to NoSQL via PySpark
Best Practices & PatternsEnglish
PITI CHAMPEETHONG
Audit and evaluate AI generated Python code
Cheuk Ting Ho
Day 1 • Sat, Oct 17
13:20 - 15:50
Location
R3
Language
English
Category • Level
Best Practices & Patterns • Medium
These may be the most important skills to keep your job or to find a new one in the future!
AI-generated code is a powerful tool, but it should never be blindly trusted. This intensive workshop, "Audit and evaluate AI generated Python code," teaches you the critical skills you need to stay relevant in the future: how to ensure AI-generated code is secure, functional, and aligns with your standards.
Description
We will go beyond basic reviews with a four-step, hands-on approach. You will learn how to:
- Perform Functional Verification using tools like pytest and hypothesis for property-based testing;
- Conduct Security & Dependency Audits with static analysis tools;
- Execute an Adversarial Review by attacking your own program; and
- Achieve Architectural Alignment using ruff and type checking with ty.
Finally, we will cover how to measure and document the performance of your AI prompts using evaluation metrics. You’ll leave this workshop prepared to take control of code that AI produces in your Python projects, and learn how to turn this code into quality code.
After having a career as a Data Scientist and Developer Advocate, Cheuk dedicated her work to the open-source community. Currently, she is working as AI developer advocate for JetBrains. She has co-founded Humble Data, a beginner Python workshop that has been happening around the world. She has served the EuroPython Society board for two years and is now a fellow and director of the Python Software Foundation.
