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You are conducting multiple machine learning experiments using PySpark on Databricks. How can MLflow be utilized to manage these experiments effectively, ensuring both reproducibility and tracking of model performance?
A
Implement custom logging within your PySpark scripts to track model performance metrics.
B
Store all models in Azure Blob Storage and manually log experiment results in an Excel sheet.
C
Use MLflow Projects to package the PySpark ML code, track experiments with MLflow Tracking, and register models with MLflow Model Registry.
D
Rely solely on Databricks notebooks' revision history to track changes in ML experiments.
