{"skill":{"slug":"skill-109","displayName":"Skill 109","summary":"Expertise in deploying, monitoring, detecting drift, automating retraining, and ensuring fairness and compliance for production ML models.","tags":{"latest":"1.0.0"},"stats":{"comments":0,"downloads":277,"installsAllTime":0,"installsCurrent":0,"stars":0,"versions":1},"createdAt":1772974722169,"updatedAt":1777525735122},"latestVersion":{"version":"1.0.0","createdAt":1772974722169,"changelog":"Skill 109: MLOps & Model Governance v1.0.0\n\n- Initial release covering the MLOps lifecycle from deployment and versioning to monitoring and governance.\n- Details common deployment patterns (batch, real-time API, stream), model versioning, and canary release strategy.\n- Introduces data quality checks, feature store concepts, and production data validation approaches.\n- Explains model drift types and detection, automated retraining pipelines, and rollback procedures.\n- Outlines model and business metrics for monitoring, plus real-time observability dashboards.\n- Addresses governance: fairness checks, bias mitigation, compliance, and Model Card documentation.","license":null},"metadata":null,"owner":{"handle":"timbohnett-farther","userId":"publishers:timbohnett-farther","displayName":"timbohnett-farther","image":"https://avatars.githubusercontent.com/u/229849801?v=4"},"moderation":null}