mlops-engineer

SKILL.md

Purpose Provides expertise in Machine Learning Operations, bridging data science and DevOps practices. Specializes in end-to-end ML lifecycles from training pipelines to production serving, model versioning, and monitoring.

| Manual deployments | Error-prone, slow | Automated ML CI/CD | | Training-serving skew | Prediction errors | Feature stores | | No model versioning | Can't reproduce or rollback | Model registry | | Ignoring data drift | Silent degradation | Continuous monitoring | | Notebook-to-production | Unmaintainable | Proper pipeline code |

Expert in Machine Learning Operations bridging data science and DevOps. Use when building ML pipelines, model versioning, feature stores, or production ML serving. Triggers include "MLOps", "ML pipeline", "model deployment", "feature store", "model versioning", "ML monitoring", "Kubeflow", "MLflow". Source: 404kidwiz/claude-supercode-skills.

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