Pycon and Pydata Germany 2022

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My Notes on Talks I like:

1. Flexible ML Experiment Tracking System for Python Coders with DVC and Streamlit: Talks about Versioning of Machine Learning models and data with DVC along with visualisation in Streamlit.

2. Navigating the limitations of Python’s concurrency model in web services: Evolution of concurrency in Python on Web - unicorn to uvicorn and from Flask / Django to FastAPI.

3. Speed up python on single core: A basic recap of how you can speed up Python code without parallelisation.

My Notes on other talks / Tutorials I attended:

1. MLflow tutorial
2. Data unit tests (mostly about Great Expectations)
3. Easy Python lies - what can make Python code complex
4. ML testing ecosystem - How we can audit ML models against vulnerability
5. Processing Open Street Map Data with Python and PostgreSQL