{"skill":{"slug":"ml-ops","displayName":"Ml Ops","summary":"Deep MLOps workflow—reproducible training, experiment tracking, packaging, deployment, monitoring (drift, performance), governance, and rollback for ML. Use...","tags":{"latest":"1.0.0"},"stats":{"comments":0,"downloads":144,"installsAllTime":0,"installsCurrent":0,"stars":0,"versions":1},"createdAt":1774397569946,"updatedAt":1774399907053},"latestVersion":{"version":"1.0.0","createdAt":1774397569946,"changelog":"- Initial release of the \"ml-ops\" skill featuring a comprehensive MLOps workflow.\n- Covers reproducible training, experiment tracking, packaging, deployment, monitoring (drift, performance), governance, and rollback.\n- Introduces six workflow stages: problem & risk class, data & reproducibility, training & evaluation, packaging & deployment, monitoring & feedback, governance & rollback.\n- Provides practical triggers, stage exit conditions, and a final review checklist.\n- Includes tips for preventing common pitfalls and adapting practices for LLM products or small teams.","license":"MIT-0"},"metadata":null,"owner":{"handle":"clawkk","userId":"s170g5yz1q3ksjnn4gz6v24af983h1mh","displayName":"clawkk","image":"https://avatars.githubusercontent.com/u/265748372?v=4"},"moderation":null}