Databricks Machine Learning Associate Question 377
Select 3A data science team has trained a machine learning model in Databricks and now wants to distribute the model to multiple environments for scalable predictions. The team decides to use MLflow for this purpose. Which of the following steps are required to successfully distribute the model using MLflow?
- A
Register the trained model in the MLflow Model Registry.
- B
Package the model as a Docker container using MLflow's built-in functionality.
- C
Deploy the model to a Databricks-hosted cluster or an external serving platform.
- D
Convert the model into a Spark MLlib pipeline before deployment.
- E
Log the model to MLflow during training to enable versioning and reproducibility.
Show answer and explanation
Correct answers: A, C, E
Explanation
MLflow simplifies the process of model distribution by providing tools for logging, registering, and deploying models. Logging the model during training ensures reproducibility, while registering the model enables version control and lifecycle management. Finally, deploying the model to a serving platform or cluster allows for scalable predictions in production environments.
- A. Correct.
Registering the trained model in the MLflow Model Registry is a key step for managing the model's lifecycle, including versioning, stage transitions (e.g., Staging to Production), and enabling deployment to multiple environments.
- B. Incorrect.
While MLflow supports Docker-based deployment, it does not have built-in functionality to package models directly as Docker containers. This step is not specific to MLflow's required workflow.
- C. Correct.
Deploying the model to a Databricks-hosted cluster or an external serving platform is an essential step in scaling the model for production use cases.
- D. Incorrect.
Converting the model into a Spark MLlib pipeline is not required unless the model specifically needs Spark-based transformations. This is not a general requirement for model distribution with MLflow.
- E. Correct.
Logging the model to MLflow during training ensures that the model is stored with its associated metadata and artifacts, enabling reproducibility and easier deployment.