Making LLM Agents Observable & Debuggable

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 h

Claire is an AI leader, a MLOps and data science practitioner, and an advocate and mentor for women in the AI industry. She started her career as a statistician and worked her way up to a data scientist role at Trunk Club where she specialized in building personalization algorithms and outfit recommenders. After personally feeling the challenges of bringing models to production, she focused her career on MLOps and LLMOps best practices. She moved to Twilio and then Opendoor where she built and led engineering teams and platform teams focused on researching and deploying models at scale. She holds a master’s in statistics and a bachelor’s in applied mathematics.