Databricks Machine Learning Associate Question 1
Select 3You are tasked with training a machine learning model on Databricks to predict customer churn. The dataset is stored as a Delta table, and you want to keep track of your experiments and their corresponding metrics during the development process. Which of the following steps should you take to efficiently manage your machine learning workflow on Databricks?
- A
Use MLflow to log the parameters, metrics, and models for each experiment.
- B
Directly train the model without logging any details to avoid unnecessary overhead.
- C
Use Databricks Feature Store to create and track reusable features in your pipeline.
- D
Store all training and evaluation metrics in a local JSON file for record-keeping purposes.
- E
Leverage the Databricks AutoML feature to quickly generate baseline models and insights.
Show answer and explanation
Correct answers: A, C, E
Explanation
To efficiently manage a machine learning workflow on Databricks, it is crucial to leverage tools such as MLflow for experiment tracking, Databricks Feature Store for feature management, and Databricks AutoML for quickly generating baseline models. These tools are specifically designed to enhance productivity and reproducibility. Avoiding proper logging or relying solely on local storage methods decreases the efficiency and scalability of your workflow.
- A. Correct.
Correct: MLflow is integrated into Databricks and is specifically designed for tracking parameters, metrics, and models in a machine learning workflow.
- B. Incorrect.
Incorrect: Not logging any details will make it difficult to track and compare experiments, leading to poor reproducibility and inefficient development.
- C. Correct.
Correct: Databricks Feature Store allows you to manage and reuse features across different models, improving consistency and efficiency in machine learning workflows.
- D. Incorrect.
Incorrect: While storing metrics in a local JSON file may work for small projects, it is not an efficient or scalable solution compared to MLflow or other built-in Databricks tools.
- E. Correct.
Correct: Databricks AutoML can be used to automatically generate baseline models, saving time and providing initial insights that can guide further development.