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Aurimas Griciūnas, Chief Product Officer at Neptune.AI, dives into the complexities of training large language models and the critical need for effective experiment tracking. He discusses the transition from MLOps to LLMOps and how traditional tools struggle with the data demands of foundation models. Griciūnas highlights the challenges of operating massive GPU clusters and the importance of checkpoints for fault tolerance. The episode also covers breakthroughs in AI reasoning and the fine-tuning approaches essential for enterprises navigating this evolving landscape.