Databricks Machine Learning Professional Question 31
Select 3You are working on a machine learning project in Databricks and want to compare the performance of multiple models trained during hyperparameter tuning. You decide to use MLflow for tracking experiments. Which of the following actions must you take to ensure the experiment results are properly logged and can be compared effectively?
- A
Set up an experiment using
mlflow.set_experiment()before training the models. - B
Log all relevant metrics, parameters, and artifacts using MLflow logging functions.
- C
Ensure that each model is trained in its own separate Databricks workspace.
- D
Use a unique run ID for each training iteration within the experiment.
- E
Enable model versioning in MLflow before starting the experiment.
Show answer and explanation
Correct answers: A, B, D
Explanation
To effectively track and compare experiments in MLflow, you must organize runs under the correct experiment, log all relevant information (metrics, parameters, artifacts), and ensure unique identification for each run. These steps ensure reproducibility and allow you to analyze model performance systematically. Model versioning and workspace separation are not necessary for basic experiment tracking.
- A. Correct.
Correct: Setting up an experiment using
mlflow.set_experiment()ensures that all runs are logged under the correct experiment. Without this, runs may be logged under the default experiment, making it difficult to organize and compare results. - B. Correct.
Correct: Logging metrics, parameters, and artifacts is essential for comparing model performance and reproducing the results. MLflow provides functions like
mlflow.log_param()andmlflow.log_metric()for this purpose. - C. Incorrect.
Incorrect: Training models in separate Databricks workspaces is unnecessary. MLflow can handle multiple runs and experiments within the same workspace, as long as they are properly organized.
- D. Correct.
Correct: Using a unique run ID for each training iteration ensures that each run is individually identifiable and its results can be properly tracked and compared within the experiment.
- E. Incorrect.
Incorrect: While MLflow does support model versioning, it is not required for tracking experiments. Model versioning is used mainly for managing production-ready models, not for logging and comparing experimental runs.