Databricks Machine Learning Professional Question 54
Select 3You are tasked with developing and deploying a machine learning model for a retail company to predict customer churn. During the model lifecycle, you decide to use Databricks' MLflow for tracking and managing the model. Which of the following is the best practice for ensuring proper model lifecycle management in this scenario?
- A
Log all relevant parameters, metrics, and artifacts during model experimentation.
- B
Directly deploy the model to production without performing any versioning.
- C
Use MLflow Model Registry to manage model versions and transitions between stages.
- D
Automate the deployment process to move models from experimentation to production without validation.
- E
Assign appropriate access controls to the MLflow Model Registry to ensure roles and permissions are followed.
Show answer and explanation
Correct answers: A, C, E
Explanation
Proper model lifecycle management involves logging all experimental details for reproducibility, using the MLflow Model Registry to version and manage models, and ensuring access controls are in place to maintain security and compliance. Skipping versioning or validation can lead to issues in production, so these practices should be avoided.
- A. Correct.
Logging parameters, metrics, and artifacts in MLflow ensures that the model's performance and configuration are reproducible and traceable, which is crucial for effective model lifecycle management.
- B. Incorrect.
Directly deploying a model to production without versioning is a poor practice because it can lead to confusion, lack of traceability, and potential risks if errors occur.
- C. Correct.
Using the MLflow Model Registry is essential for managing model versions, tracking their lifecycle stages (e.g., staging, production), and ensuring a structured approach to deployment.
- D. Incorrect.
Automating deployment without validation can lead to untested or flawed models being deployed into production, which can cause significant business issues.
- E. Correct.
Assigning appropriate access controls to the MLflow Model Registry ensures that only authorized users can modify or deploy models, which is critical for maintaining security and compliance.