MLOps – Definition & Meaning

Definition of MLOps

: the practices and systems used to develop, test, package, deploy, monitor, govern, and update machine-learning models reliably in production
- The fraud model passed through the MLOps pipeline before the new version was released to customers.
- Good MLOps alerted the team when changes in live transaction data began to reduce the model's accuracy.

Origin & history

MLOps combines machine learning with DevOps. The practice grew from the operational problems encountered when machine-learning models moved from experiments into production; by the late 2010s and early 2020s, major cloud platforms and research surveys were using MLOps for the automation and monitoring of the machine-learning lifecycle.

Test yourself

Which of these is the meaning of MLOps?

Related terms

concept drift a change over time in the statistical relationship between a model's inputs and the outcome it… continual learning an approach in which a machine-learning system learns from a sequence or stream of changing… eval a set of test cases used to measure how well a model or AI feature performs on the tasks you… data labeling the work of tagging raw data with correct answers or categories, such as marking objects in… AI infrastructure the integrated hardware, networking, data, software, and physical facilities used to train… model routing the automatic selection of which AI model, model tier, or model provider should handle a… Edge MLOps MLOps adapted for deploying, monitoring, and updating machine-learning models across edge…

Cite this entry

"MLOps." AI Dictionary, Dadgogo, https://dadgogo.com/dictionary/mlops/. Accessed 9 Oct. 2026.