Designing and Implementing a Data Science Solution on Azure — Question 107

You manage an Azure Machine Learning workspace.

You build a custom model you must log with MLflow. The custom model includes the following:

• The model is not natively supported by MLflow.
• The model cannot be serialized in Pickle format.
• The model source code is complex.
• The Python library for the model must be packaged with the model.

You need to create a custom model flavor to enable logging with MLflow.

What should you use?

Answer options

Correct answer: A

Explanation

The correct answer is A, as a model loader is specifically designed to handle non-native models and manage complex dependencies. The other options do not provide the necessary functionality for creating a custom flavor to log models that cannot be easily serialized or are complex in nature.