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.

Skylar Payne specializes in rescuing AI systems that work in demos but fail in production. As founder of Wicked Data LLC, he diagnoses and fixes broken retrieval systems for startups and enterprises alike. Previously, as VP of Engineering & Data Science at HealthRhythms, he built monitoring systems that caught silent ML failures before they reached users. At LinkedIn, he led teams that increased experimentation velocity 3x while moving core metrics by double digits through improved recommendation systems. At Google, he developed testing infrastructure for large-scale distributed systems. Skylar has personally debugged retrieval systems across every scale—from early prototypes to platforms serving hundreds of millions—and brings battle-tested patterns that bridge the gap between ML experiments and reliable products.