LLMOps refers to the practices and tools used to deploy, manage, and monitor large language models in production. It covers the full lifecycle — data preparation, model tuning, deployment, evaluation, and ongoing optimization.
LLMs have unique operational characteristics: they are large, expensive to run, sensitive to prompt changes, and tend to produce qualitatively different failure modes than traditional ML models. LLMOps adapts MLOps principles to these characteristics, providing the operational discipline needed to keep LLM-powered systems reliable and compliant at scale.
