Databricks Machine Learning Associate Question 4
Single answerYou are building a machine learning model on Databricks using Databricks Machine Learning. After creating a notebook and setting up your feature engineering pipeline, you want to track and compare the performance of multiple models trained with different hyperparameters. Which feature should you use to achieve this in Databricks?
- A
Databricks Repos
- B
MLflow Tracking
- C
AutoML
- D
Databricks SQL Analytics
Show answer and explanation
Correct answer: B
Explanation
MLflow Tracking, integrated into Databricks Machine Learning, enables data scientists and engineers to log and compare metrics, parameters, and artifacts from different model runs. This makes it the ideal tool for tracking and comparing the performance of multiple models with varying hyperparameters.
- A. Incorrect.
Databricks Repos is used for managing and version controlling code, not for tracking and comparing model performance.
- B. Correct.
MLflow Tracking is the correct feature to use for logging and comparing model parameters, metrics, and artifacts across different runs.
- C. Incorrect.
AutoML automates the process of building machine learning models but is not specifically used for tracking or comparing model performance.
- D. Incorrect.
Databricks SQL Analytics is focused on querying and analyzing data using SQL, not on tracking machine learning models.