ml-pipeline-workflow
End-to-end MLOps pipeline orchestration covering data prep, training, validation, deployment, and monitoring.
Install
npx skills add https://github.com/wshobson/agents --skill ml-pipeline-workflowStats
| Total installs | 3,468 |
| Weekly installs | 3.4K |
| GitHub stars | 32.4K |
| First seen | Jan 20, 2026 |
| Source | @wshobson/agents |
Summary
- Covers five core pipeline stages: data preparation, model training, validation, deployment, and monitoring with DAG orchestration patterns (Airflow, Dagster, Kubeflow)
- Includes data validation, feature engineering, experiment tracking integration, and model versioning strategies across the full ML lifecycle
- Provides deployment automation patterns including canary releases, blue-green deployments, A/B testing infrastructure, and rollback mechanisms
- References and templates available for pipeline DAGs, training configuration, and pre-deployment validation checklists
Tags
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FAQ
- How many installs does ml-pipeline-workflow have?
- ml-pipeline-workflow has 3,468 total installs and 3.4K installs this week.
- Where is ml-pipeline-workflow hosted?
- ml-pipeline-workflow is published by @wshobson/agents at https://github.com/wshobson/agents.
- How many GitHub stars does ml-pipeline-workflow have?
- ml-pipeline-workflow has 32.4K GitHub stars.
- When was ml-pipeline-workflow first indexed?
- OrangeBot.AI first indexed ml-pipeline-workflow on Jan 20, 2026.
- How do I install ml-pipeline-workflow?
- Run: npx skills add https://github.com/wshobson/agents --skill ml-pipeline-workflow