MLOps
Definition
MLOps organizes reproducible development, deployment, and monitoring of ML systems.
Practices for reproducible development, deployment, monitoring, and governance of ML systems.
Simply explained
How to organize the ongoing operation of a model workshop, with audits, responsibilities, and traceable changes.
Why it matters in practice
Helps develop and operate digital systems robustly, securely, and maintainably.
Example from business
A model is versioned, audited, gradually rolled out, and monitored for drift.
Limits and pitfalls
- Reducing MLOps to a single tool or omitting data, domain metrics, and responsibilities is a mistake.
Origin & history
MLOps combines DevOps principles with the additional data and model risks of ML.
Use in mid-sized companies
- Possible example: Domain and operations teams define model versions, release criteria, monitoring, and fallback paths. Changes are audited and responsibilities for data and model operations documented.
Frequently asked questions
What does operation of machine learning systems (MLOps) mean?
Practices for reproducible development, deployment, monitoring, and governance of ML systems.
Which mistakes should be avoided?
Reducing MLOps to a single tool or omitting data, domain metrics, and responsibilities is a mistake.
Sources
Sources checked on 12.10.2026. Editorial use cases are hypothetical; no evidence of real customer results.
Editorially reviewed on: