Hi there 👋 I'm Stéphane Wirtel
Senior Python Expert | CPython Core Developer
- 🐍 CPython Core Developer since 2019
- 🎤 International conference speaker (PyCon, EuroPython, FOSDEM)
- 📝 Technical writer & blogger at wirtel.be
- 🥋 2nd DAN Karate JKA
- 🏢 Founder of MGX.IO - Python consulting
📚 Latest Blog Posts
➡️ Read more on my blog
🚀 Featured Projects
CPython Core Developer
Contributing to the Python programming language - bug triage, documentation improvements since 2019.
Co-Founder and co-organizer of PythonFOSDEM, a community event that has grown from 80 to 800+ attendees, bringing Python enthusiasts together at FOSDEM.
PyCon Ireland co-organizer (2020-2025) & python.ie maintainer — 11 years serving Ireland's Python community through code, talks, and event organization.
SMTP debugging tool for Python developers - a project that evolved from a simple script to version 1.0.0 over 12 years.
🎤 Conference Talks
I've delivered 9 major talks across Europe and North America at conferences including:
- PyCon Ireland - Python and open source development
- PyCon Canada - CPython internals and contribution workflows
- EuroPython - Advanced Python topics
- FOSDEM - Community building and Python ecosystem
🛠️ Tech Stack & Skills
Areas of Expertise:
- CPython internals and core development
- Machine Learning & Data Science
- Educational data mining and predictive analytics
- Web frameworks (Django, FastAPI, Wagtail)
- Database design and PostgreSQL optimization
- Web3 and blockchain development (Solidity, smart contracts)
- Financial systems and risk modeling
- Open source community leadership
- DevOps & Infrastructure automation (Ansible, Docker, Traefik)
- System administration and large-scale Debian migrations
🤖 Machine Learning & AI
Classical ML & Data Science
Recent Projects:
- 📊 Educational Predictive Analytics - ML model to identify and support students at risk of dropout
- 🔄 End-to-End ML Pipelines - From data exploration to production deployment with testing and CI/CD
- 📈 Feature Engineering - Creating business-driven features (success rates, performance trends, segmentation)
ML/Data Science Stack:
- Core ML: scikit-learn (pipelines, preprocessing), pandas, numpy
- Visualization: matplotlib, seaborn, plotly
- Tools: Jupyter notebooks
- MLOps: Reproducible builds, model versioning with joblib, CI/CD for ML
Deep Learning & NLP
DiagnosIA - Medical Diagnosis Classification System
Built a production-ready medical diagnosis system using Bio_ClinicalBERT for symptom-to-disease classification with 6 disease classes.
Key Achievements:
- Implemented transfer learning with Bio_ClinicalBERT fine-tuning
- Designed intelligent medical data augmentation (shuffle, drop, templates) preserving clinical meaning
- Achieved production deployment with 4 user interfaces (CLI, REST API, Streamlit, Chainlit)
- Architected BERT + LLM hybrid system: BERT for prediction, LLM for clinical summaries
- Multi-backend LLM support (Ollama local, Lightning.AI cloud)
Deep Learning Stack:
Core Skills:
- Transfer learning & model fine-tuning
- Text classification with transformers (BERT family)
- Production ML pipelines (training, evaluation, deployment)
- Multi-device optimization (CUDA, MPS, CPU)
- LLM integration and prompt engineering
- ML application development (API, Web UI, Chat)
📊 GitHub Stats
📫 Connect With Me
🎯 Currently
- 📖 Writing and relaunching my Python 3.14 book
- 🐍 Contributing to CPython and maintaining the Developer Guide
- 🎪 Organizing PythonFOSDEM community events
- 💼 Available for Python consulting and speaking opportunities
- 🏗️ Building infrastructure automation with Ansible and Docker
- 🌱 Exploring the intersection of Python and blockchain technologies
- 🤖 Building Deep Learning applications with transformers and LLMs (see DiagnosIA project)
Last updated: January 2026