25 au 27 février, 2026
Montréal, Canada
Conférence Python
La liste des présentations est sujette à changement sans préavis.
Python
Cette session est une plongée technique dans la création d'un pipeline de diagnostic assisté par IA. Nous verrons comment enchaîner trois modèles de vision par ordinateur pour analyser des radiographies et détecter pneumonie, COVID-19 et tuberculose. Puis, nous utiliserons les résultats pour interroger un LLM via une architecture RAG afin de générer un rapport préliminaire. Un cas d'usage de bout en bout qui montre la puissance de la combinaison.
Python
Command-line tools are potent, but they can also be visually appealing. In this talk, we'll explore how Rich, a Python library for beautiful formatting and text-based user interfaces, can help us transform simple CLIs into engaging TUIs.
By the end of the session, you'll know how to bring your CLIs to life, making them not only more functional but also more enjoyable to use.
By the end of the session, you'll know how to bring your CLIs to life, making them not only more functional but also more enjoyable to use.
Python
Building supportive AI assistants requires the engineering of conversational context without dilution. We will cover the stages of conversation: setting initial context foundations; using summarizers and memory types (short/long/persistent) as conversations grow; and leveraging RAG for permanent knowledge and graph structures for dynamic information on entities. Includes practical implementation details like audio capabilities and observability.
Python
On dit que les données sont l’or noir du 21e siècle. Cependant, la qualité des données dans les organisations est toujours pointée du doigt comme étant insuffisante pour en tirer une valeur. Dans cette conférence, nous allons passer en revue les techniques peuvent aider à améliorer la qualité et la valeur des données, comme les quality checks, le data observability, les data contract et le data mesh. Le tout accompagné d'exemples pratiques.
Python
Large Language Models are everywhere, but how they actually work “under the hood” often remains a mystery. In this live-coding talk, we’ll skip the slides and build an LLM step by step, not just a toy model, but a state-of-the-art one. Along the way we’ll dive into tokenization, transformer architecture, small-scale training, and inference, showing how these components come together to power today’s most advanced models.
Python
Python gets a new version every year, and every version gets new features.
In this talk, I'll look at the most interesting features added to Python in the last few years, with some examples of how to use them!
If you're still writing Python like it's 2015, why not learn what Python does can do in 2025?
In this talk, I'll look at the most interesting features added to Python in the last few years, with some examples of how to use them!
If you're still writing Python like it's 2015, why not learn what Python does can do in 2025?
Python
FastAPI and Pydantic revolutionized API building in Python with strong typing, validation, and auto-docs. But generative AI often breaks that structure, messy JSON, brittle parsing, endless debugging. Pydantic AI brings the same rigor to AI agents: typed, validated, model-agnostic, observable. This talk shows how it works and how to use it to build reliable, structured AI Agent.
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