Databricks Machine Learning Professional Question 3
Select 3You are training a machine learning model to predict customer churn using Databricks MLflow. During the experimentation phase, you want to compare the performance of several models with different hyperparameter configurations. Which of the following practices will help you effectively manage and compare your experiments in Databricks?
- A
Use MLflow to log key metrics, parameters, and artifacts for each experiment run.
- B
Manually track hyperparameters and metrics in a local spreadsheet to avoid dependency on MLflow.
- C
Organize runs by assigning meaningful tags such as model type or dataset version.
- D
Use the Databricks REST API to programmatically fetch and compare experiment results for large-scale analysis.
- E
Perform all model training and experimentation in local environments to ensure reproducibility.
Show answer and explanation
Correct answers: A, C, D
Explanation
Effective experimentation in Databricks requires leveraging tools like MLflow for tracking metrics, parameters, and artifacts, as well as organizing experiments with meaningful tags. Additionally, Databricks REST API facilitates large-scale analysis of runs, making it an important part of the experimentation workflow. Avoiding manual tracking methods or limiting experimentation to local environments ensures scalability and reproducibility.
- A. Correct.
Using MLflow to log key metrics, parameters, and artifacts is crucial for tracking and comparing runs efficiently within the Databricks platform.
- B. Incorrect.
Manually tracking hyperparameters and metrics in a local spreadsheet is error-prone and not scalable, especially when working on multiple experiments.
- C. Correct.
Assigning meaningful tags to runs helps organize experiments and makes it easier to filter and compare results later on.
- D. Correct.
Using the Databricks REST API to programmatically fetch experiment results is a scalable and efficient way to analyze and compare multiple runs.
- E. Incorrect.
Performing all experimentation in local environments can hinder reproducibility, as Databricks provides built-in support for logging, tracking, and sharing results.