Maxime Labonne

Maxime Labonne, a Senior Staff Machine Learning Scientist at Liquid AI, and Paul-Emil Iusztin, founder of Decoding ML, delve into the fascinating world of AI application development. They tackle the complexities of deploying AI models, from understanding fine-tuning nuances to the critical RAG feature pipeline. Strategies for effective problem definition and the importance of scalable solutions are highlighted. The discussion also covers managing deployment costs and the balance between safety and model customization, making the technical accessible and engaging.

Maxime Labonne, Head of Post-Training at Liquid AI and author of the LLM Engineers Handbook, dives into the future of AI design. He emphasizes that growth is achieved not just through bigger models, but through smarter, more efficient ones. Maxime explores the critical role of data quality, suggesting accuracy and diversity are key. He also discusses the challenges of deploying AI on edge devices and critiques the current hype surrounding AI technologies, advocating for a more nuanced understanding of their capabilities.

Dans ce podcast, Maxime Labonne, Head of Post-Training chez Liquid AI, évoque son parcours impressionnant, de la cybersécurité aux modèles de langage avancés. Il explique les défis du post-training et le fine-tuning supervisé, tout en présentant les Liquid Foundation Models qui transforment l'IA. Maxime parle aussi des innovations en matière d'architectures, des enjeux des workflows de développement assistés par l'IA et l'importance de l'open-source pour l'innovation en intelligence artificielle.