hugo bowne-anderson
Hugo Bowne-Anderson is an independent data and AI consultant with extensive experience in the tech industry. He is the host of the industry Vanishing Gradients, where he explores cutting-edge developments in data science and artificial intelligence.
As a data scientist, educator, evangelist, content marketer, and strategist, Hugo has worked with leading companies in the field. His past roles include Head of Developer Relations at Outerbounds, a company committed to building infrastructure for machine learning applications, and positions at Coiled and DataCamp, where he focused on scaling data science and online education respectively.
Hugo's teaching experience spans from institutions like Yale University and Cold Spring Harbor Laboratory to conferences such as SciPy, PyCon, and ODSC. He has also worked with organizations like Data Carpentry to promote data literacy.
His impact on data science education is significant, having developed over 30 courses on the DataCamp platform that have reached more than 3 million learners worldwide. Hugo also created and hosted the popular weekly data industry podcast DataFramed for two years.
Committed to democratizing data skills and access to data science tools, Hugo advocates for open source software both for individuals and enterprises.
Sessions
This workshop is designed to equip software engineers with the skills to build and iterate on generative AI-powered applications. Participants will explore key components of the AI software development lifecycle through first principles thinking, including prompt engineering, monitoring, evaluations, and handling non-determinism. The session focuses on using multimodal AI models to build applications, such as querying PDFs, while providing insights into the engineering challenges unique to AI systems. By the end of the workshop, participants will know how to build a PDF-querying app, but all techniques learned will be generalizable for building a variety of generative AI applications.
If you're a data scientist, machine learning practitioner, or AI enthusiast, this workshop can also be valuable for learning about the software engineering aspects of AI applications, such as lifecycle management, iterative development, and monitoring, which are critical for production-level AI systems.
Large language models (LLMs) enable powerful data-driven applications, but many projects get stuck in “proof-of-concept purgatory”—where flashy demos fail to translate into reliable, production-ready software. This talk introduces the LLM software development lifecycle (SDLC)—a structured approach to moving beyond early-stage prototypes. Using first principles from software engineering, observability, and iterative evaluation, we’ll cover common pitfalls, techniques for structured output extraction, and methods for improving reliability in real-world data applications. Attendees will leave with concrete strategies for integrating AI into scientific Python workflows—ensuring LLMs generate value beyond the prototype stage.