ml-pipeline-automation
Automate ML workflows with Airflow, Kubeflow, MLflow. Use for reproducible pipelines, retraining schedules, MLOps, or encountering task failures, dependency errors, experiment tracking issues.
npx skills add majiayu000/claude-skill-registry --skill ml-pipeline-automation --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 Automation Orchestrate end-to-end machine learning workflows from data ingestion to production deployment with production-tested Airflow, Kubeflow, and MLflow patterns. ## When to Use This Skill Load this skill when: - **Building ML Pipelines**: Orchestrating data → train → deploy workflows - **Scheduling Retraining**: Setting up automated model retraining schedules - **Experiment Tracking**: Tracking experiments, parameters, metrics across runs - **MLOps Implementation**: Building reproducible, monitored ML infrastructure - **Workflow Orchestration**: Managing complex multi-step ML workflows - **Model Registry**: Managing model versions and deployment lifecycle ## Quick Start: ML Pipeline in 5 Steps ```bash # 1. Install Airflow and MLflow (check for latest versions at time of use) pip install apache-airflow==3.1.5 mlflow==3.7.0 # Note: These versions are current as of December 2025 # Check PyPI for latest stable releases: https://pypi.org/project/apache-airflow/ # 2. Initialize Airflow database airflow db init # 3. Create DAG file: dags/ml_training_pipeline.py cat > dags/ml_training_pipeline.py << 'EOF' from airflow import DAG from airflow.operators.python import Pyt
- When to Use This Skill
- Quick Start: ML Pipeline in 5 Steps
- Core Concepts
- Pipeline Stages
- Orchestration Tools Comparison
- Basic Airflow DAG
- Known Issues Prevention
- 1. Task Failures Without Alerts
- 2. Missing XCom Data Between Tasks
- 3. DAG Not Appearing in UI
- 4. Hardcoded Paths Break in Production
- 5. Stuck Tasks Consume Resources
- 6. No Data Validation = Bad Model Training
- 7. Untracked Experiments = Lost Knowledge
pip install apache-airflow==3.1.5 mlflow==3.7.0 Check PyPI for latest stable releases: https://pypi.org/project/apache-airflow/ airflow db init cat > dags/ml_training_pipeline.py << 'EOF' airflow scheduler & airflow webserver --port 8080 & airflow dags trigger ml_training_pipeline Access UI: http://localhost:8080 Check for syntax errors python dags/my_dag.py
What does the ml-pipeline-automation skill do?
Automate ML workflows with Airflow, Kubeflow, MLflow. Use for reproducible pipelines, retraining schedules, MLOps, or encountering task failures, dependency errors, experiment tracking issues.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ml-pipeline-automation --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.