Jamie Coombes
I am a Machine Learning Engineer with 4 years of Python and PyTorch development experience. As a tech lead, I've managed the project discussed in this talk, providing first-hand experience with the architectural decisions and implementation challenges of building production language model applications.
I've provided ML expertise to startups and the UK government, and I'm particularly interested in beneficial AI applications. My background is in Physics and Atmospheric Physics, where I interpreted large tropical cyclone datasets at Imperial College London.
My most recent three Python and PyCon talks are a language exploration trilogy, bringing together insights from Python, Mojo, and Rust into practical application development with language models:
PyCon/PyData DE 2025 (Darmstadt) - 🦀 Rüstzeit: Asynchronous Concurrency in Python & Rust
PyCon/PyData DE 2023 (Berlin) - Mojo 🔥 - Is it Python's faster cousin or just hype?
EuroPython Prague 2022 - 🐍 Large Language Model Zen
This talk returns to Language Model Zen three years on, showing how the theoretical possibilities discussed then have evolved into $10bn applications today.
Session
It's easy to get into sticky situations when building language model powered applications. This talk helps you transform sticky messes into sweet successes.
This talk delivers practical insights from creating production-ready language model solutions with Python. Moving beyond theoretical possibilities, we'll explore the real architectural decisions, code patterns, and tradeoffs that determine success or failure. Through a progressive case study, we'll demonstrate how to effectively leverage Pydantic structured outputs, validation techniques, and thoughtful API integrations while avoiding common pitfalls. Perfect for Pythonic product teams looking to build applications that result in a sweet treat everyone will love.