ML Pipeline Automation

This covers the core components you'd expect in an ML pipeline: data ingestion, processing, model training, validation, deployment, and monitoring. It's aimed at orchestrating the full workflow rather than solving one specific piece. The skill references common platforms like Airflow, Kubeflow, and MLflow, so it likely helps you work with those tools through Claude. If you're tired of manually stitching together different parts of your ML workflow or need to make your experiments more reproducible, this is worth checking out. The repo has 245 stars and has passed security audits from Gen Agent Trust Hub, Socket, and Snyk.