mlops-advanced
Implement advanced MLOps practices for production ML systems. Use for: building CI/CD pipelines for ML models, implementing continuous training and monitoring, managing model registries and versioning, deploying with blue-green and canary strategies, monitoring model drift and performance degradation, orchestrating ML workflows with Kubeflow/Airflow, implementing feature stores, ensuring model governance and compliance, and scaling ML infrastructure with Kubernetes.
npx skills add majiayu000/claude-skill-registry --skill mlops-advanced --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 Advanced Implement production-grade MLOps practices for scalable, reliable machine learning systems. ## Overview MLOps (Machine Learning Operations) extends DevOps principles to machine learning, automating and streamlining the entire ML lifecycle from development to deployment and monitoring. Advanced MLOps addresses unique ML challenges including data dependencies, model versioning, continuous training, performance monitoring, and infrastructure scaling. It encompasses CI/CD for ML, automated retraining, drift detection, and governance frameworks. ## MLOps Maturity Levels **Level 0 - Manual Process:** - Manual data preparation and training - Notebook-driven development - Manual deployment - No CI/CD or monitoring **Level 1 - ML Pipeline Automation:** - Automated training pipelines - Continuous training (CT) - Model and data validation - Automated deployment of prediction service **Level 2 - CI/CD Pipeline Automation:** - Full CI/CD system for ML - Automated testing (data, model, infrastructure) - Rapid experimentation and deployment - Comprehensive monitoring and logging ## CI/CD for Machine Learning ### Continuous Integration Components ```yaml # .github/workflows/ml-ci.
- Overview
- MLOps Maturity Levels
- CI/CD for Machine Learning
- Continuous Integration Components
- Continuous Deployment
- Model Registry and Versioning
- MLflow Model Registry
- Model Versioning Strategy
- Deployment Strategies
- Blue-Green Deployment
- Canary Deployment
- Continuous Monitoring
- Model Performance Monitoring
- Data Drift Detection
What does the mlops-advanced skill do?
Implement advanced MLOps practices for production ML systems. Use for: building CI/CD pipelines for ML models, implementing continuous training and monitoring, managing model registries and versioning, deploying with blue-green and canary strategies, monitoring model drift and performance degradation, orchestrating ML workflows with Kubeflow/Airflow, implementing feature stores, ensuring model governance and compliance, and scaling ML infrastructure with Kubernetes.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill mlops-advanced --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.