CI/CD for ML
Testing pipelines that emit models, data-dependent tests, progressive delivery and automated retraining.
5concepts
62flashcards
36minutes 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.
- 03 Automated Retraining and Its Triggers When a retraining loop is worth building, the four trigger designs and what each optimises, and the failure modes that make automated retraining actively dangerous.
- 04 Data-Dependent Tests and Behavioural Suites Why aggregate metrics are a poor gate, how curated behavioural cases catch regressions that averages hide, and the discipline of turning every production failure into a permanent test.
- 05 Progressive Delivery for Models The staged rollout patterns that separate correctness validation from quality validation, why shadow deployment answers a question canary cannot, and what each stage should be watching.