Databricks Machine Learning Professional Question 47
Select 3You are tasked with training a machine learning model using Hyperopt for hyperparameter tuning in Databricks. You want to enable MLflow autologging to automatically track model training metrics, parameters, and artifacts. Which of the following steps are required to properly enable autologging for this process?
- A
Call mlflow.autolog() before running the Hyperopt optimization process.
- B
Ensure that Hyperopt is wrapped within an MLflow experiment context by using mlflow.start_run().
- C
Import and configure the Hyperopt library before enabling MLflow autologging.
- D
Call mlflow.hyperopt.fmin() to enable automatic logging of Hyperopt trials.
- E
Enable autologging for specific libraries, such as XGBoost or TensorFlow, when using those libraries with Hyperopt.
Show answer and explanation
Correct answers: A, B, D
Explanation
To enable autologging in Databricks with Hyperopt, you need to use mlflow.autolog() to enable tracking, ensure the optimization process is wrapped in an MLflow run context with mlflow.start_run(), and use mlflow.hyperopt.fmin() to log Hyperopt trials. These steps ensure that hyperparameter tuning and associated metrics and artifacts are tracked automatically. Additional configurations for specific libraries may be helpful but are not mandatory for enabling Hyperopt autologging.
- A. Correct.
Correct: Calling mlflow.autolog() is necessary to enable autologging for tracking metrics, parameters, and models across various libraries.
- B. Correct.
Correct: Wrapping the Hyperopt optimization process within mlflow.start_run() ensures that MLflow can track the experiment and log results within an active context.
- C. Incorrect.
Incorrect: While configuring Hyperopt is necessary, it does not directly relate to enabling MLflow autologging. This step is not required for autologging itself.
- D. Correct.
Correct: When using Hyperopt, mlflow.hyperopt.fmin() is needed to log Hyperopt trials automatically to MLflow, making this step essential.
- E. Incorrect.
Incorrect: Autologging for specific libraries (e.g., XGBoost or TensorFlow) is useful when using those libraries, but it is not a required step for enabling Hyperopt autologging itself.