Glossary
MLOps
The engineering discipline for building, deploying, and monitoring ML systems.
Definition
MLOps (machine learning operations) is the set of practices for reliably building, deploying, monitoring, and iterating on machine learning systems in production — the ML equivalent of DevOps. It covers versioning of data and models, deployment pipelines, monitoring for drift, and automated retraining.
Context
MLOps was a specialist discipline for classical ML through the 2010s. In the LLM era it has expanded to LLMOps — versioning of prompts, evaluations as part of the pipeline, cost and latency monitoring, and safety observability. Organizations mature enough to run production AI at scale invest heavily in the tooling that makes updates safe.