CI/CD for Model Pipelines

Model changes flow through automated pipelines that build, test, validate, and stage artifacts — the same rigor as software delivery, adapted to models and machines.

Continuous integration for models

A change to training code, a feature definition, or a dataset should not require a human to remember every downstream check. Kronos runs model changes through CI/CD pipelines that automatically rebuild the affected artifacts, run the full test and validation suite, and stage what passes. The pipeline is the mechanism that makes the MLOps lifecycle repeatable rather than heroic.

Model CI extends software CI with data- and model-specific stages. Beyond unit tests on transforms and feature code, the pipeline runs reproducibility checks, the full validation gate suite, parity checks against the compiled edge form, and regression tests against a frozen benchmark of recorded machine states. A regression against the incumbent blocks the pipeline.

Pipeline stages

• Lint and unit-test transform / feature / training code
• Rebuild affected datasets and features (with lineage)
• Reproducible retrain; reproducibility gate
• Validation gates + parity + calibration
• Regression vs incumbent on frozen state benchmark
• Register artifact to STAGING on full pass

# Pipeline definition (declarative)
stages:
  - test:      run unit + property tests
  - build:     rebuild data + features, emit lineage
  - train:     deterministic retrain from manifest
  - validate:  gates(accuracy,calibration,safety,parity,repro)
  - regress:   compare vs incumbent on benchmark; fail on regression
  - stage:     registry.register(state='STAGING')   # never PROD here

CI/CD never promotes to PROD automatically; it can only reach STAGING. The machine-facing promotions — SHADOW, CANARY, PROD — require the deliberate, human-gated steps in the governance workflow. Automation removes toil and enforces consistency; it does not remove the human decision to grant a model authority over the breeder or burner.