Definition
MLOps is the set of engineering and operational practices used to build, version, test, deploy, observe, govern, and maintain machine-learning systems throughout their lifecycle. It extends software delivery with concerns such as data lineage, reproducible training, experiment tracking, model registries, feature pipelines, drift, evaluation, and retraining.
Distinguish it from nearby terms
DevOps primarily manages software and infrastructure delivery. MLOps adds learned artifacts and the data-dependent behavior that produces them. LLMOps is a narrower and often application-heavy specialization for language-model systems.
Check your understanding
A model endpoint in production is not an MLOps capability. Identify how data, code, parameters, evaluations, approvals, deployments, monitoring, rollback, and ownership are versioned together.