AI-900: Efficient Model Tracking: Best Practices for Managing Multiple Versions in Azure Machine Learning

Learn the essential steps to effectively track multiple versions of a model trained with Azure Machine Learning. Discover the importance of provisioning an inference cluster, explaining the model, registering the model, and managing the training data. Streamline your model management process and ensure accurate version control with Azure Machine Learning’s robust features.

Question

You need to track multiple versions of a model that was trained by using Azure Machine Learning. What should you do?

A. Provision an inference duster.
B. Explain the model.
C. Register the model.
D. Register the training data.

Answer

C. Register the model.

Explanation

The correct answer is C. Register the model.

To track multiple versions of a model that was trained by using Azure Machine Learning, you should register the model in your workspace. Model registration allows you to store and version your models in the Azure cloud, and keep track of the associated metadata required to use the model. You can register models from different sources, such as local files, run outputs, or web URLs. You can also tag and annotate your models with additional information, such as the model description, framework, or domain. By registering your models, you can easily access and manage them for deployment, testing, or further experimentation.

Provisioning an inference cluster is not the correct answer, because it is a way to deploy a model as a web service, not to track its versions. An inference cluster is a scalable cluster of virtual machines that hosts the web service endpoint for your model.

Explaining the model is not the correct answer, because it is a way to understand the model’s behavior and interpret its predictions, not to track its versions. Explaining the model can help you identify the features that have the most impact on the model output, and detect any potential biases or errors in the model.

Registering the training data is not the correct answer, because it is a way to create and share datasets, not to track model versions. Registering the training data can help you access and reuse the data for different experiments, and capture the data lineage and provenance information.

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