During model deployment, an analytics team discovers the production data pipeline delivers a feature in a different unit than the training data used. What quality assurance practice would most likely have caught this earlier?
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A
Data validation checks comparing training and production schemas
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B
Increasing the model's regularization strength
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C
Running the model on a larger training dataset
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D
Adding more stakeholders to the sign-off meeting