AI-900: Building ML Pipelines in Azure with Datasets, Computes and Modules

Azure Machine Learning designer provides a visual interface to construct end-to-end ML workflows. Learn how key components like datasets, computes and modules can be dragged onto the canvas.

Question

Which two components can you drag onto a canvas in Azure Machine Learning designer? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. dataset
B. compute
C. pipeline
D. module

Answer

A. dataset
D. module

Explanation

You can drag-and-drop datasets and modules onto the canvas.

The correct answer is A and D. You can drag both datasets and modules onto a canvas in Azure Machine Learning designer.

A dataset is a data asset that you can use as an input or output for a component in a pipeline. You can create a dataset from various data sources, such as Azure Blob Storage, Azure Data Lake Storage, Azure SQL, or local files. You can also register a dataset in your workspace and access it from the asset library in the designer. A dataset can only connect to a component, not to another dataset.

A module is a component that performs a specific machine learning task, such as data transformation, feature engineering, model training, model evaluation, or model deployment. You can use the built-in modules provided by Azure Machine Learning, or create your own custom modules using Python or R code. You can also share and reuse modules across different pipelines and workspaces. A module can connect to either a dataset or another module, depending on its input and output specifications.

A compute is a cloud resource that you can use to run your pipeline jobs. You can configure a compute target for each pipeline draft in the designer. You can also create and manage compute targets from the Azure Machine Learning studio. A compute is not a component that you can drag onto a canvas.

A pipeline is a workflow of connected components that defines the steps of your machine learning project. You can create a pipeline visually by dragging and dropping components onto a canvas in the designer. You can also edit, run, and publish pipelines from the designer. A pipeline is not a component that you can drag onto a canvas, but rather the result of connecting components.

References

Microsoft Docs > Azure > Machine Learning > What is Azure Machine Learning designer?

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