Esc
↑↓ navigate   Enter open   Esc close

Data Science & ML

MLOps

/em el ɒps/

Definition

Machine Learning Operations — combining ML development with DevOps practices for reliable model deployment and monitoring.

Example in context

"MLOps pipeline: train → evaluate → register → deploy → monitor for drift — automated end to end in Kubeflow."

Related terms

  • Model drift — The degradation of a model's accuracy over time as real-world data patterns diverge from its training distribution.
  • CI/CD — Continuous Integration / Continuous Deployment — a practice of automatically building, testing, and releasing code on every change.

Practice this term

Master MLOps in context by working through exercises in the Data Science & ML module. You'll see the term used in real engineering scenarios with multiple-choice, fill-in-the-blank, and matching drills.

Practice in exercises → Browse all words

Frequently Asked Questions

What does "MLOps" mean?

Machine Learning Operations — combining ML development with DevOps practices for reliable model deployment and monitoring.

How do you pronounce "MLOps"?

"MLOps" is pronounced /em el ɒps/.

Can you use "MLOps" in a sentence?

"MLOps pipeline: train → evaluate → register → deploy → monitor for drift — automated end to end in Kubeflow."