ml-pipeline
Designs and implements production-grade ML pipeline infrastructure: configures experiment tracking with MLflow or Weights & Biases, creates Kubeflow or Airflow DAGs for training orchestration, builds feature store schemas with Feast, deploys model registries, and automates retraining and validation workflows. Use when building ML pipelines, orchestrating training workflows, automating model lifecycle, implementing feature stores, managing experiment tracking systems, setting up DVC for data versioning, tuning hyperparameters, or configuring MLOps tooling like Kubeflow, Airflow, MLflow, or Pref
npx skills add Jeffallan/claude-skills --skill ml-pipeline --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.
# ML Pipeline Expert Senior ML pipeline engineer specializing in production-grade machine learning infrastructure, orchestration systems, and automated training workflows. ## Core Workflow 1. **Design pipeline architecture** — Map data flow, identify stages, define interfaces between components 2. **Validate data schema** — Run schema checks and distribution validation before any training begins; halt and report on failures 3. **Implement feature engineering** — Build transformation pipelines, feature stores, and validation checks 4. **Orchestrate training** — Configure distributed training, hyperparameter tuning, and resource allocation 5. **Track experiments** — Log metrics, parameters, and artifacts; enable comparison and reproducibility 6. **Validate and deploy** — Run model evaluation gates; implement A/B testing or shadow deployment before promotion ## Reference Guide Load detailed guidance based on context: | Topic | Reference | Load When | |-------|-----------|-----------| | Feature Engineering | `references/feature-engineering.md` | Feature pipelines, transformations, feature stores, Feast, data validation | | Training Pipelines | `references/training-pipelines.md` | Train
- Core Workflow
- Reference Guide
- Code Templates
- MLflow Experiment Logging (minimal reproducible example)
- Kubeflow Pipeline Component (single-step template)
- Data Validation Checkpoint (Great Expectations style)
- Constraints
- Output Format
- Knowledge Reference
What does the ml-pipeline skill do?
Designs and implements production-grade ML pipeline infrastructure: configures experiment tracking with MLflow or Weights & Biases, creates Kubeflow or Airflow DAGs for training orchestration, builds feature store schemas with Feast, deploys model registries, and automates retraining and validation workflows. Use when building ML pipelines, orchestrating training workflows, automating model lifecycle, implementing feature stores, managing experiment tracking systems, setting up DVC for data versioning, tuning hyperparameters, or configuring MLOps tooling like Kubeflow, Airflow, MLflow, or Pref
How do I install it?
Run `npx skills add Jeffallan/claude-skills --skill ml-pipeline --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 Jeffallan/claude-skills, a repository with 10,871 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.