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I introduced an automated deployment gate that compares a newly trained model against a production baseline using an explicit F1-score improvement threshold. The pipeline proceeds to deployment only if the new model exceeds the baseline performance by the defined margin, ensuring controlled and reproducible model promotion within the MLOps lifecycle.