ml-ops
Use this skill when deploying ML models to production, setting up model monitoring, implementing A/B testing for models, or managing feature stores. Triggers on model deployment, model serving, ML pipelines, feature engineering, model versioning, data drift detection, model registry, experiment tracking, and any task requiring machine learning operations infrastructure.
npx skills add majiayu000/claude-skill-registry --skill ml-ops --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.
When this skill is activated, always start your first response with the 🧢 emoji. # ML Ops A production engineering framework for the full machine learning lifecycle. MLOps bridges the gap between model experimentation and reliable production systems by applying software engineering discipline to ML workloads. This skill covers model deployment strategies, experiment tracking, feature stores, drift monitoring, A/B testing, and versioning - the infrastructure that makes models trustworthy over time. Think of it as DevOps for models: automate everything, measure what matters, and treat reproducibility as a first-class constraint. --- ## When to use this skill Trigger this skill when the user: - Deploys a trained model to a production serving endpoint - Sets up experiment tracking for training runs (parameters, metrics, artifacts) - Implements canary or shadow deployments for a new model version - Designs or integrates a feature store for online/offline feature serving - Sets up monitoring for data drift, prediction drift, or model degradation - Runs A/B or champion/challenger tests across model versions in production - Versions models, datasets, or pipelines with DVC or a model regis
- When to use this skill
- Key principles
- Core concepts
- Common tasks
- Design an ML pipeline
- Set up experiment tracking
- Deploy a model with canary rollout
- Implement model monitoring
- Build a feature store
- A/B test models in production
- Version models and datasets
- Anti-patterns / common mistakes
- Gotchas
- References
Track a dataset in DVC dvc add data/training/users_2024q1.parquet git add data/training/users_2024q1.parquet.dvc .gitignore git commit -m "Track Q1 2024 training dataset" Push dataset to remote storage dvc push Reproduce dataset at a specific git commit git checkout <commit-hash> dvc pull
What does the ml-ops skill do?
Use this skill when deploying ML models to production, setting up model monitoring, implementing A/B testing for models, or managing feature stores. Triggers on model deployment, model serving, ML pipelines, feature engineering, model versioning, data drift detection, model registry, experiment tracking, and any task requiring machine learning operations infrastructure.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ml-ops --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.