mlops
Implement MLOps practices for ML lifecycle management. Use for CI/CD pipelines, model versioning, experiment tracking, automated training, deployment automation, monitoring, and production ML workflows.
npx skills add majiayu000/claude-skill-registry --skill mlops --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 Implement MLOps practices for managing the complete ML lifecycle. ## Overview MLOps applies DevOps principles to machine learning, enabling reliable, scalable, and automated ML systems. This skill covers pipelines, versioning, monitoring, and production workflows. ## Quick Reference | Scenario | Recommended Approach | Reference File | |----------|---------------------|----------------| | Experiment tracking and versioning | MLflow, Weights & Biases, DVC | `/references/tracking.md` | | Automated training and deployment | CI/CD pipelines, Kubeflow, Airflow | `/references/automation.md` | | Production monitoring and maintenance | Drift detection, retraining, A/B testing | `/references/production.md` | ## Core Principles 1. **Automation** - Automate training, testing, and deployment 2. **Versioning** - Track code, data, and models 3. **Reproducibility** - Ensure consistent results 4. **Monitoring** - Detect performance degradation 5. **Collaboration** - Enable team workflows ## MLOps Components ### Experiment Tracking Record experiments, metrics, and artifacts. **Tools:** - MLflow: Open-source, comprehensive - Weights & Biases: Collaborative, feature-rich - TensorBoard: Visuali
- Overview
- Quick Reference
- Core Principles
- MLOps Components
- Experiment Tracking
- Data Versioning
- Model Registry
- CI/CD for ML
- Continuous Integration
- Continuous Deployment
- Pipeline Orchestration
- Production Monitoring
- Model Performance
- Data Drift
What does the mlops skill do?
Implement MLOps practices for ML lifecycle management. Use for CI/CD pipelines, model versioning, experiment tracking, automated training, deployment automation, monitoring, and production ML workflows.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill mlops --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.