{
  "id": "lib-process:mlops--model-lifecycle",
  "_kind": "LibraryProcess",
  "_file": "generated-library/processes.yaml",
  "_cluster": "generated-library",
  "attributes": {
    "displayName": "mlops/model-lifecycle",
    "description": "Flagship end-to-end model lifecycle: dataset governance intake (parallel\n  per-dataset lineage/consent/retention checks) -> eval-harness design -> executed\n  training/eval runs -> an adversarial eval-review gate that RE-RUNS a sampled eval and\n  diffs metrics -> a policy-gated model promotion with an executed serving smoke ->\n  drift-monitoring setup with an executed drift-detection stub -> an adversarial\n  drift-review gate -> a drift path with severity-routed escalation and a policy-gated\n  rollback/retirement -> kip-backed model-registry memory. model-lifecycle owns the\n  governance/eval/promotion/drift/retirement lifecycle AROUND a trained candidate;\n  training itself can be delegated to the data-science-ml near-misses\n  (model-training-pipeline.js, automl-pipeline.js) — see the README composition map.\n  Those are callable upstream stages, NOT superseded by this process.",
    "libraryPath": "library/specializations/mlops/model-lifecycle.js",
    "specialization": "mlops",
    "usesAgents": [
      "dataset-governance-checker",
      "retention-executor",
      "eval-harness-designer",
      "model-trainer",
      "eval-runner",
      "promotion-deployer",
      "promotion-verifier",
      "drift-monitor-engineer",
      "drift-triager",
      "rollback-executor",
      "rollback-verifier"
    ]
  },
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}