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A data scientist is building an end-to-end machine learning pipeline in Python using pandas, scikit-learn, and TensorFlow, involving data cleaning, feature engineering, model training, and deployment. Repetitive tasks, like writing data transformation functions and evaluation metrics, slow development. How can GitHub Copilot streamline this workflow and boost productivity?