MLOps emerged to solve the problem of getting machine learning models into production reliably. It covers model training, versioning, deployment, monitoring, data pipelines, and performance management.
Agent systems overlap with MLOps, but they are not the same thing. Agents add tool use, reasoning, workflow execution, permissions, retrieval, and human approval paths. For many SMBs, LLMOps or AI operations will be more relevant than traditional MLOps.