Challenges in economics and governance models for open-source scientific projects

In this presentation, the CEOs of two companies at the forefront of open-source scientific software development - Sylvain Corlay of QuantStack and Yann Lechelle of Probabl - examine the intricate challenges of open-source funding and governance and reflect on how these two aspects interconnect.

We start by reflecting on the origins of the open-source movement within the scientific community, and delve into the contemporary challenges of operating businesses and identifying sustainable economic models that both leverage and contribute to open-source software.

In particular, we highlight the unique approaches and experiences of QuantStack and Probabl, which primarily contribute to multi-stakeholder scientific projects such as scikit-learn, Jupyter, Apache Arrow, or conda-forge.

This talk presents a technical case study of applying agentic AI systems to automate community operations at PyCon DE & PyData, treated as an open-source testbed. The key lesson is simple: AI only works when put on a leash. Reliable results required good architecture, a clear plan, and structured data models — from YAML and Pydantic schemas to reproducible pipelines with GitHub Actions. With that foundation, LLM agents supported logistics, FAQs, video processing, and scheduling; without it, they failed. By contrasting successes and failure modes across different coding agents, the talk demonstrates that robust design, validation, and controlled context are prerequisites for making agentic AI usable in real-world workflows.

Using AI agents and automation, PyCon DE & PyData volunteers have transformed chaos into streamlined conference ops. From YAML files to LLM-powered assistants, they automate speaker logistics, FAQs, video processing, and more while keeping humans focused on creativity. This case study reveals practical lessons on making AI work in real-world scenarios: structured workflows, validation, and clear context beat hype. Live demos and open-source tools included.

An exchange of views on fastAPI in practice.

FastAPI is great, it helps many developers create REST APIs based on the OpenAPI standard and run them asynchronously. It has a thriving community and educational documentation.

FastAPI does a great job of getting people started with APIs quickly.

This talk will point out some obstacles and dark spots that I wish we had known about before. In this talk we want to highlight solutions.

We talked about:

Alexander’s background The role of Partner at Königsweg Being part of the data and AI community How Alexander became chair at PyData Alexander’s many talks and advice on giving them Explaining AI to managers Why being able to explain machine learning to managers is important The experimentational nature of AI and why it’s not a cure-all Innovation requires patience Convincing managers not to use AI or ML when there are better (simpler) solutions The role of MLOps in enterprises Thinking about the mid- and long-term when considering solutions Finding Alexander online

Links: 

Alexander's Twitter: https://twitter.com/hendorf Alexander's LinkedIn: https://www.linkedin.com/in/hendorf/ Königsweg: https://www.koenigsweg.com PyData Südwest: https://www.meetup.com/pydata-suedwest/ PyData Frankfurt: https://www.meetup.com/pydata-frankfurt/ PyConDE & PyData Berlin: https://pycon.de

ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

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