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The ML Pipeline Workflow skill provides a standardized framework for building robust, production-grade machine learning pipelines. It guides developers through the entire MLOps lifecycle, including data validation, feature engineering, model training, and automated deployment strategies like canary or blue-green releases. By implementing DAG-based orchestration patterns and experiment tracking, this skill ensures that ML workflows are reproducible, scalable, and observable, making it ideal for teams transitioning from experimental notebooks to integrated production systems.
