CI/CD for Machine Learning
The short answer · 18 words
In short
Transfer of continuous integration and continuous delivery to machine learning systems, including code, data, models and ML pipelines.
Flow-alerta.cab (2026): CI/CD for Machine Learning. https://flow-alerta.cab/en/glossar/ci-cd-for-machine-learning. Editorially reviewed on: 09 October 2026.
How much detail would you like?
Practical
Relevant for reproducible, safe and maintainable ML systems that are regularly modified or retrained.
Example scenario: After a change to a feature pipeline, data, model and integration tests are run; only if the quality limits are met, the new version is rolled out.
For productive ML applications, define tests for data, pipeline, model quality and interfaces and secure deployments with versioning and rollback.
At a glance
- Core
- CI/CD for machine learning extends classic software pipelines with ML-specific checks: data and schema tests, pipeline tests, model evaluation, reproducible training artifacts and controlled deployment. Continuous training can be added as an additional process, but is not the same as CI/CD.
- Use
- Relevant for reproducible, safe and maintainable ML systems that are regularly modified or retrained.
- Example
- Example scenario: After a change to a feature pipeline, data, model and integration tests are run; only if the quality limits are met, the new version is rolled out.
- Limits
- Only test code; ignore training data and model metrics; equate continuous training with deployment; roll out models without versioning and rollback.
In context
Questions about this term
Is ML-CI/CD the same as classic CI/CD?
No, data and models are added.
Is automatic retraining mandatory?
No.
Does this include monitoring?
For a resilient operating process, yes.
All details and background
CI/CD for machine learning extends classic software pipelines with ML-specific checks: data and schema tests, pipeline tests, model evaluation, reproducible training artifacts and controlled deployment. Continuous training can be added as an additional process, but is not the same as CI/CD.
ELI5 – Explained simply
Not only is the program code automatically tested and delivered, but also data, training and model steps are controlled.
Why it matters in practice
Relevant for reproducible, safe and maintainable ML systems that are regularly modified or retrained.
Example
Example scenario: After a change to a feature pipeline, data, model and integration tests are run; only if the quality limits are met, the new version is rolled out.
Pitfalls and limits
- Only test code; ignore training data and model metrics; equate continuous training with deployment; roll out models without versioning and rollback.
Origin & history
MLOps transfers DevOps principles to machine learning and complements CI/CD with data, model and training aspects. Continuous training can be added as an additional process.
Use in mid-sized companies
- For productive ML applications, define tests for data, pipeline, model quality and interfaces and secure deployments with versioning and rollback.
Related terms
- MLOs
- Model Registry
- ML pipeline
- Canary Deployment
Matching Flow-alerta service
Websites & web applicationsWebsites & web applications: technical implementation and testing of a suitable web application.Matching tool / use case
View use casesUse Case: automated test and deployment pipeline for an ML model with quality limits.Sources
- cloud.google.comGoogle Cloud, MLOps: Continuous delivery and automation pipelines in machine learning
Quellenprüfung: 09.10.2026. Definition und dokumentierte Einordnung abgeglichen; Beispiele beschreiben mögliche Anwendungen.
Editorially reviewed on: