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The ML Pipeline Workflow skill provides a comprehensive framework for building production-grade machine learning pipelines with an emphasis on reproducibility and modularity. It guides users through the entire ML lifecycle, including DAG-based orchestration for tools like Airflow or Kubeflow, automated data validation, experiment tracking, and sophisticated deployment patterns such as canary or blue-green releases. By implementing these standardized patterns, developers can ensure their ML systems are observable, testable, and ready for high-scale production environments while maintaining strict data lineage and quality controls.
