Model monitoring is an essential part of the machine learning lifecycle, ensuring that models function correctly and efficiently. Recently, at MLOps World in Austin, discussions revolved around innovative LLM techniques such as transforming embedding spaces and using logit_bias for classification problems. However, challenges remain, including fine-tuning cycles, LLM evaluation, and ensuring context-derived responses. Despite these obstacles, the field of machine learning continues to evolve with new solutions emerging to tackle these issues.