MLOps(Machine Learning Operations) and LLMOps are concepts concerned with managing the life cycles of ML models. The areas of focus differ.

MLOps is a broader term that cover the operations processes for all types of ML models — efficient development, deployment and monitoring of these models.

LLMOps is specifically designed for LLMs. These models are used for NLP and LLMOps addresses the unique challenges associated with the lifecycle of these complex models.

Both these concepts of have some common goals — efficiency, reliability and fairness. They have some distinct considerations. The metrics relied upon are accuracy, precision and recall for MLOps. LLMs use more nuanced metrics such as BLEU and ROUGE to assess language fluency and coherence. LLMs, in addition, put a premium on interpretability, fairness and bias mitigation.

MLOps are adaptable across various ML domains. LLMOps are specialized.