Discover the components you can drag onto a canvas in Azure Machine Learning designer. Learn about datasets and modules and how they are used to build ML pipelines.
Table of Contents
Question
Which components can you drag onto a canvas in Azure Machine Learning designer? Select two correct options.
A. dataset.
B. pipeline.
C. compute.
D. module
Answer
A. dataset.
D. module
Explanation
In Azure Machine Learning designer, you can drag the following two components onto a canvas:
A. Dataset – Datasets represent the data you will use to train and test your machine learning models. You can add datasets to the canvas and connect them to modules for data preprocessing, model training, and evaluation.
D. Module – Modules are the building blocks of machine learning pipelines in Azure ML designer. Each module performs a specific operation on the data, such as data transformation, feature selection, model training, or evaluation. You drag and connect modules on the canvas to define the workflow of your ML pipeline.
The other two options are incorrect:
B. Pipeline – In Azure ML designer, a pipeline refers to the entire workflow you create by connecting datasets and modules on the canvas. You don’t drag a pipeline onto the canvas; instead, you build it on the canvas.
C. Compute – Compute refers to the computational resources (like CPU or GPU) used to run your ML pipeline. You don’t directly drag compute resources onto the canvas in Azure ML designer. Instead, you specify the compute target when setting up your pipeline’s execution.
In summary, datasets (A) and modules (D) are the components you can drag onto a canvas in Azure Machine Learning designer to build your machine learning pipelines.
Dataset: You can drag datasets onto the canvas to use as input for your machine learning workflows.
Module: Modules represent specific operations or functions that you can apply to your data, such as data transformation, training models, or evaluation.
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