Explanation:
The correct option is D. mlflow.build_docker_image. Here's why:
mlflow.build_docker_image.Therefore, mlflow.build_docker_image is the most appropriate command for this scenario, enabling the engineer to build a customized and production-ready Docker image for their MLflow model.
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A senior machine learning engineer is looking to deploy a model as a Docker container and expose it as a REST API in a production environment using MLflow. Which MLflow command or functionality should they use to create a Docker image for the model?
A
mlflow.dockerize_model
B
mlflow.create_container
C
mlflow.serve_model
D
mlflow.build_docker_image