Databricks Machine Learning Associate Question 5
Single answerA data scientist is working on a machine learning project in Databricks and needs to track experiments, manage model versions, and transition models to production. Which Databricks Machine Learning feature should they use to accomplish all these tasks?
- A
Databricks Repos
- B
MLflow
- C
Databricks Feature Store
- D
Spark MLlib
Show answer and explanation
Correct answer: B
Explanation
MLflow is the correct answer because it is an integral part of Databricks Machine Learning and provides end-to-end support for experiment tracking, model versioning, and transitioning models into production environments. These capabilities are essential for managing the machine learning lifecycle effectively.
- A. Incorrect.
Databricks Repos is used for version controlling notebooks and integrating with Git, but it does not provide features for experiment tracking, model versioning, or production transitions.
- B. Correct.
MLflow is a tool integrated with Databricks Machine Learning to track experiments, manage model versions, and facilitate the deployment process, making it the correct choice.
- C. Incorrect.
Databricks Feature Store is used for managing and serving machine learning features, but it does not provide experiment tracking or model versioning capabilities.
- D. Incorrect.
Spark MLlib is a machine learning library for distributed computation, but it does not offer experiment tracking, model versioning, or deployment management.