mlops/model-lifecycle overview

Flagship end-to-end model lifecycle: dataset governance intake (parallel per-dataset lineage/consent/retention checks) -> eval-harness design -> executed training/eval runs -> an adversarial eval-review gate that RE-RUNS a sampled eval and diffs metrics -> a policy-gated model promotion with an executed serving smoke -> drift-monitoring setup with an executed drift-detection stub -> an adversarial drift-review gate -> a drift path with severity-routed escalation and a policy-gated rollback/retirement -> kip-backed model-registry memory. model-lifecycle owns the governance/eval/promotion/drift/retirement lifecycle AROUND a trained candidate; training itself can be delegated to the data-science-ml near-misses (model-training-pipeline.js, automl-pipeline.js) — see the README composition map. Those are callable upstream stages, NOT superseded by this process.