A tabular model requires handling missing numeric values. The team plans median imputation. Which implementation is most appropriate?

1. Replace missing values differently in training and serving without evaluation.
2. Compute the median from the complete future evaluation dataset at prediction time.
3. Drop every row with a missing value without checking population impact.
4. Fit imputation on training data, persist the artifact, and reuse it consistently in validation and serving.