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The ML Pipeline Workflow skill provides a comprehensive framework for building and managing production-grade machine learning lifecycles. It guides users through the implementation of modular, idempotent pipelines using DAG orchestration patterns for data ingestion, feature engineering, model validation, and deployment. By incorporating best practices for versioning, observability, and failure handling, this skill ensures that ML models are reproducible, scalable, and ready for high-stakes production environments.
