Delve into the essential tasks that necessitate the use of an enterprise workspace in Azure Machine Learning. Discover the benefits of leveraging a GUI for automated machine learning experiments and creating a compute instance for workstation purposes.
Table of Contents
Question
You are evaluating whether to use a basic workspace or an enterprise workspace in Azure Machine Learning. What are two tasks that require an enterprise workspace? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
A. Use a graphical user interface (GUI) to run automated machine learning experiments.
B. Create a compute instance to use as a workstation.
C. Use a graphical user interface (GUI) to define and run machine learning experiments from Azure Machine Learning designer.
D. Create a dataset from a comma-separated value (CSV) file.
Answer
A. Use a graphical user interface (GUI) to run automated machine learning experiments.
C. Use a graphical user interface (GUI) to define and run machine learning experiments from Azure Machine Learning designer.
Explanation
The correct answer is A. Use a graphical user interface (GUI) to run automated machine learning experiments and C. Use a graphical user interface (GUI) to define and run machine learning experiments from Azure Machine Learning designer.
Azure Machine Learning offers two types of workspaces: basic and enterprise. The basic workspace is a free tier that provides access to the core features of Azure Machine Learning, such as creating and managing datasets, compute targets, models, and endpoints. The enterprise workspace is a paid tier that provides access to additional features, such as automated machine learning, Azure Machine Learning designer, data labeling, and model interpretability.
Automated machine learning is a feature that allows you to create and optimize machine learning models automatically, without writing code. You can use a graphical user interface (GUI) in Azure Machine Learning studio to configure and run automated machine learning experiments, and view the results and metrics. This feature requires an enterprise workspace.
Azure Machine Learning designer is a feature that allows you to create and run machine learning experiments using a drag-and-drop interface, without writing code. You can use a graphical user interface (GUI) in Azure Machine Learning studio to design and execute machine learning pipelines, and publish them as web services. This feature requires an enterprise workspace.
The other options are not correct for the following reasons:
- Create a compute instance to use as a workstation: This is a task that can be done in both basic and enterprise workspaces. A compute instance is a pre-configured cloud-computing resource that you can use to train, automate, manage, and track machine learning models. You can use a compute instance to run Jupyter notebooks and Python scripts in Azure Machine Learning studio.
- Create a dataset from a comma-separated value (CSV) file: This is a task that can be done in both basic and enterprise workspaces. A dataset is a reference to data that you use for machine learning. You can create a dataset from various sources, such as CSV files, Azure Blob storage, Azure Data Lake Storage, Azure SQL Database, etc. You can use a dataset to train and evaluate machine learning models, and to create data assets.
Note: Enterprise workspaces are no longer available as of September 2020. The basic workspace now has all the functionality of the enterprise workspace.
Reference
- Azure Machine Learning
- Microsoft Learn > Azure > Machine Learning > What is an Azure Machine Learning workspace?
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