Model Versioning and Management in Azure ML
Question
While developing your machine learning models and while running experiments, you want to make use of the tools and practices provided by the DevOps-based model management environment.
As you develop your models, several versions of the model are created, need to be stored in a central place and to be made available later.
Which of the following features of Azure ML best supports your goal?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.Correct Answer: C.
Option A is incorrect because Environments describe the pip and Conda dependencies for projects, and are used for training and deployment of models.
Environments allow for the reproducibility of the builds.
Option B is incorrect because model profiling is intended to help you understand the CPU and memory requirements of the service that will be created when deploying the model.
Option C is CORRECT because it is the Model registration which allows you to store and version your models in your workspace.
The model registry provides the organization, tracking and versioning of the trained models.
Each time a model with the same name is added to the regisítry, a new version is created.
Option D is incorrect because while it is actually the versioning that will support your goal, model versioning is an implicit mechanism of the model registration process.
It is not a stand-alone feature.
Registration should be used.
References:
The feature of Azure ML that best supports the goal of storing and making multiple versions of machine learning models available is model versioning.
Model versioning is a critical aspect of machine learning model management that enables data scientists and machine learning engineers to track changes to their models, compare performance across different versions, and reproduce experiments.
By versioning models, data scientists can keep track of model changes, compare performance across different versions, and reproduce experiments. Azure ML provides an easy-to-use model versioning system that integrates with other Azure ML features and supports automated model deployment.
The other options listed in the question, Azure ML environments, model profiling, and model registration, do not directly support the goal of storing and versioning multiple models. However, they are all important components of an end-to-end machine learning workflow.
Azure ML environments enable data scientists to create and manage reproducible environments for their machine learning experiments. Model profiling provides insights into model performance, resource utilization, and other critical metrics that can help optimize model performance. Model registration allows data scientists to store their models in a central repository and manage their lifecycle.
In summary, while all of the listed features are essential for building and managing machine learning models, model versioning is the best option for storing and making multiple versions of models available.
