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Answer: Use MLflow Projects to package the PySpark ML code, track experiments with MLflow Tracking, and register models with MLflow Model Registry.
The correct approach is to use MLflow Projects for packaging your PySpark ML code along with its dependencies, which facilitates reproducibility across different environments. MLflow Tracking is essential for logging and comparing experiment parameters, metrics, and artifacts, aiding in the identification of the best performing models. Additionally, the MLflow Model Registry plays a crucial role in managing model versions, lifecycle, and deployment, ensuring models are well-documented and accessible. Alternatives such as manual logging or relying on notebook revision history lack the efficiency, functionality, and integration provided by MLflow.
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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.