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, Geekle.us, 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.
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.