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Pydantic: Making life easier with data validation

south hall 2a
30 minutes


Abstract: Data validation is a critical component of any software application, ensuring that the data processed by the application is accurate and consistent. However, data validation can often be a tedious and error-prone process, especially when dealing with complex data structures. Pydantic, a powerful and flexible data validation library for Python, simplifies the process of data validation by providing a declarative syntax that is easy to read and write.

TalkPython Libraries


In this talk, we will introduce Pydantic and discuss how it can be used to make data validation easy in Python. We will cover the following topics:

  • What is Pydantic and how does it work?
  • How to define data models using Pydantic
  • Validating and converting data using Pydantic
  • Working with complex data structures using Pydantic
  • Integrating Pydantic with other Python libraries

We will also provide real-world examples of how Pydantic has been used to simplify data validation in production applications. By the end of this talk, attendees will have a solid understanding of Pydantic and how it can be used to make data validation easy in their Python applications.

Target Audience: This talk is aimed at Python developers of all levels who are interested in simplifying the process of data validation in their applications. No prior knowledge of Pydantic is required, but a basic understanding of Python is recommended.

Conclusion: In this talk, attendees will learn how to use Pydantic to simplify data validation in their Python applications. Whether you are building a small script or a large-scale application, Pydantic can be an invaluable tool for data validation

The speaker

Bojan Miletic

Bojan Miletic

Senior Python developer and AWS Solution Architect (AWS Certified Solution Architect Associate) with more than 10 years of experience, helping AI/ML companies and their AI/data scientists turn ML models from the data lab into PoCs, MVPs, or fully functional products that convincingly prove their value to investors and other important decision-makers. By using Python and AWS as superpowers, I help get real business value from ML algorithms. And I happily consult AI scientists on how to write clean, reusable code in Python and thus save thousands (or even millions) on ML-based software deployment and development.

Speaker at world-leading Python conferences such as EuroPython, Python Ireland, PyJamas Conference,, PyBerlin, and Conf42.

Mentor at Humble Data (PyCon Africa 2020 Conference Data Workshop and PyData Global).

Co-host at Bug Hunters Cafe podcast on software bugs and how to deal with them.

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