Friederike Bauer
Friederike Bauer works as a Data Scientist for &effect and develops software solutions as a Frontend-Developer. She combines data and software development to make a difference in social sciences and public organizations.
Session
Every week, development in AI brings us another groundbreaking release, another model version, another must-have integration. In this rapidly shifting landscape, how does one build production systems that won’t be obsolete by the time you deploy them?
We'll explain how trusting in proven engineering principles from software development and machine learning, like separation of concerns and evaluation practices, became our anchor in an ever-changing landscape of AI development. We share lessons learned from building two MCP applications using FastMCP and PydanticAI. Against these challenges, we found that fundamental engineering principles provided the foundation we needed.
Participants in the process of developing AI tools will leave with practical strategies for building AI-powered systems that are flexible enough to adapt, yet stable enough to trust.