Let’s explore the advancements in ML monitoring, from early ML 1.0 to modern LLMs. We’ll emphasize solutions to the “hallucination problem” in LLMs. Then we’ll dive into a fan favorite – where we’ll focus on RAGs, and their benefits vs. challenges. This talk offers insights into the evolving strategies for maintaining robustness and accuracy in ML models and evaluating LLMs in production – particularly valuable for ML practitioners and researchers.