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This skill provides a comprehensive framework for building production-grade MLOps pipelines, covering the entire machine learning lifecycle from data ingestion to monitoring. It offers architectural guidance for DAG-based orchestration, robust data validation, and automated deployment strategies like canary and blue-green releases. By using this skill, developers can ensure reproducibility, modularity, and scalability in their machine learning workflows while integrating seamlessly with industry-standard tools like Airflow, Kubeflow, and MLflow.
