05-18, 09:30–10:30 (Europe/Vilnius), Saphire ABC Main
Pydantic is a data validation library for Python that has seen massive adoption over the last few years - it's used by major datascience and ML libraries like Spacy, Huggingface and jinja-ai - overall Pydantic is downloaded over 55m times a month!
In this talk Samuel Colvin, the creator of Pydantic will cover two subjects which have seen massive interest in recent years:
- How Pydantic can be used to prepare data for machine learning thereby saving time and avoiding errors
- The emergence of Rust as the go-to language for high performance python libraries - how this might go in the future, and the benefits and drawbacks of the trend
In this talk I'll give a brief introduction to Pydantic, what it can do and how it differs from other similar libraries.
I'll then go on to walk through an example of how Pydantic can be used to prepared data to train a machine learning model, including some advantages of Pydantic over dataclasses or regular dictionaries.
Finally I'll give a high level introduction to how Rust is being used to build python extensions, and why that's (mostly) a great thing for the community and the planet. The two main case studies will be the recent re-write of Pydantic in Rust for V2, and Polars.
Intermediate
What topics define your talk the best? –python, PyData, data science, web API, other programming languages
Open source Python and Rust developer, maintainer of Pydantic and other libraries.
Samuel recently founded Pydantic Services Inc. to build great developer tools by applying the same principles that have made Pydantic so successful.