ml-model-lifecycle
Carries a predictive model from user-agreed success criteria and a one-time data split through baseline reference scores, single-change experiments logged in MLflow, leakage containment with user-confirmed cleanup, one final holdout measurement, and a user-approved release followed by drift monitoring and replacement rules. Use when the user is building or improving a predictive model, setting up experiment tracking, choosing a winning candidate, deciding release readiness, or investigating degraded performance after launch. Not for serving infrastructure, data-pipeline engineering, or MLOps platform administration.
- Version
- v1.0.0
- Size
- 18 KB
- Updated
- 2026-10-12
$ skilvault read --manual ml-model-lifecycleYour client verifies the signature before reading. This explicit manual read is available on every plan. Free automatically loads five chosen skills; paid plans automatically load relevant skills across the library.