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To ensure consistency in model evaluation metrics across different MLflow tracking servers, what is the most effective method?
A
Manually export and import MLflow experiments between servers for cross-comparison.
B
Consolidate all tracking servers into one before running experiments to avoid cross-version evaluation needs.
C
MLflow does not support cross-version or cross-server model evaluation comparisons; rely on external tools.
D
Use the MLflow REST API to fetch model metrics from each server and compare them using custom scripts.
