CI/CD for ML
Testing pipelines that emit models, data-dependent tests, progressive delivery and automated retraining.
6concepts
69flashcards
44minutes of reading
- 01 Build Once, Promote the Artefact Why rebuilding per environment reintroduces every difference the pipeline was meant to eliminate, how configuration is separated from the immutable artefact, and where ML deployments break the pattern.
- 02 Testing Pipelines That Emit Models What can be tested deterministically in an ML codebase, why model quality is not a unit test, and the layered test strategy that keeps CI fast while still catching the failures that matter.