mlops-engineer
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".
npx skills add majiayu000/claude-skill-registry --skill mlops-engineer-skill-404kidwiz-claude-supercode-ski --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# MLOps Engineer ## 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. ## When to Use - Building ML training and serving pipelines - Implementing model versioning and registry - Setting up feature stores - Deploying models to production - Monitoring model performance and drift - Automating ML workflows (CI/CD for ML) - Implementing A/B testing for models - Managing experiment tracking ## Quick Start **Invoke this skill when:** - Building ML pipelines and workflows - Deploying models to production - Setting up model versioning and registry - Implementing feature stores - Monitoring production ML systems **Do NOT invoke when:** - Model development and training → use `/ml-engineer` - Data pipeline ETL → use `/data-engineer` - Kubernetes infrastructure → use `/kubernetes-specialist` - General CI/CD without ML → use `/devops-engineer` ## Decision Framework ``` ML Lifecycle Stage? ├── Experimentation │ └── MLflow/Weights & Biases for tracking ├── Training Pipeline │ └── Kubeflow/Airflow/Vertex AI ├── Model Registry │
- Purpose
- When to Use
- Quick Start
- Decision Framework
- Core Workflows
- 1. ML Pipeline Setup
- 2. Model Deployment
- 3. Model Monitoring
- Best Practices
- Anti-Patterns
What does the mlops-engineer skill do?
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".
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill mlops-engineer-skill-404kidwiz-claude-supercode-ski --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.