Alex Strick van Linschoten

In a fascinating discussion, Alex Strick van Linschoten, a machine learning engineer at ZenML and curator of the LLM Ops Database, delves into the complexities of multi-agent systems. He emphasizes the dangers of introducing too many agents, advocating for simplicity and reliability. Alex shares key insights from nearly 1,000 real-world deployments, highlighting the importance of MLOps hygiene, human-in-the-loop strategies, and using basic programming checks over costly LLM judges. His practical advice on scaling down systems is a must-listen for AI developers!

Alex Strick van Linschoten, a Machine Learning Engineer at ZenML with a PhD in History, delves into practical applications of large language models (LLMs). He shares insights from his comprehensive database on LLM use cases, emphasizing both common and innovative applications. The discussion covers the technical challenges of deploying LLMs, the significance of engineering practices, and the evolution of support bots using user behavior insights. Alex also calls for community contributions to enhance collective knowledge in this rapidly changing field.

Hugo chats with Alex Strick van Linschoten, a Machine Learning Engineer at ZenML, who has documented over 400 real-world LLM deployments. They discuss the challenges in deploying AI agents, like hallucinations and cascading failures. Alex reveals practical lessons from corporate giants like Anthropic and Klarna, focusing on structured workflows that enhance reliability. He highlights the evolution of LLM capabilities and shares case studies that underscore the importance of prompt engineering and effective error handling in building robust AI systems.