How to Build and Evaluate AI systems in the Age of LLMs
A practical guide for data scientists and engineers - Hugo Bowne-Anderson
As AI moves from experimentation to real-world impact, the challenges are no longer just technical. They’re about design, evaluation, and collaboration. In this episode, Hugo will share his perspective on how teams and individuals can build AI responsibly, work effectively across disciplines, and keep learning as the field continues to change.
He’ll cover:
When (and when not) to build AI agents
Using AI for coding vs. building software with LLMs
The AI software development lifecycle and escaping “PoC purgatory”
What happens to data science in the age of AI
About the Speaker
Hugo Bowne-Anderson is an independent data and AI consultant with extensive experience in the tech industry. He has advised and taught teams building AI-powered systems, including engineers from Netflix, Meta, and the U.S. Air Force. He is the host of Vanishing Gradients and High Signal, podcasts exploring developments in data science and AI.
Previously, Hugo served as Head of Developer Relations at Outerbounds and held roles at Coiled and DataCamp, where his work in data science education reached over 6 million learners. He has taught at Yale University, Cold Spring Harbor Laboratory, and conferences like SciPy and PyCon, and is a passionate advocate for democratizing data skills and open-source tools. He also regularly teaches courses on Building LLM Applications for Data Scientists and Software Engineers.
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