Databricks Machine Learning Professional Question 30
Select 3You are a data scientist working on a machine learning project in Databricks. You want to compare multiple models with different hyperparameters to identify the best-performing one. Which of the following steps are necessary to effectively track and manage your experiments in Databricks?
- A
Log model parameters, metrics, and artifacts using MLflow's tracking API.
- B
Manually record the hyperparameters used for each experiment in a spreadsheet for future reference.
- C
Use a unique experiment name or ID to organize runs related to the same experiment.
- D
Visualize and compare the performance of different runs using the Databricks notebook interface.
- E
Set up a Databricks widget to automatically stop an experiment run when the model's accuracy exceeds a threshold.
Show answer and explanation
Correct answers: A, C, D
Explanation
To effectively manage experiments in Databricks, it is important to leverage MLflow's tracking API to log parameters, metrics, and artifacts. Organizing runs using unique experiment names or IDs ensures better management and retrieval of results. Additionally, Databricks provides visualization tools to compare runs, making it easier to identify the best-performing model. Manual tracking or misusing widgets is not recommended for effective experiment tracking.
- A. Correct.
Correct: Logging model parameters, metrics, and artifacts using MLflow's tracking API is essential for systematically tracking experiments in Databricks.
- B. Incorrect.
Incorrect: Manually recording hyperparameters in a spreadsheet is error-prone and not recommended when MLflow provides built-in tracking capabilities.
- C. Correct.
Correct: Using a unique experiment name or ID allows you to organize and retrieve related runs efficiently in Databricks.
- D. Correct.
Correct: Databricks provides visualization tools to compare the performance of different runs, which is a key part of experiment management.
- E. Incorrect.
Incorrect: While Databricks widgets are useful for interactivity, they are not designed for automatically stopping experiment runs based on model performance thresholds.