Discover the crucial steps to set up your Azure Machine Learning pipeline using Azure Machine Learning designer. Learn what you need to create before running the pipeline, including a Jupyter notebook, a registered model, and a compute resource. Optimize your model development process and streamline your machine learning workflows with Azure Machine Learning.
Table of Contents
Question
You use Azure Machine Learning designer to build a model pipeline. What should you create before you can run the pipeline?
A. a Jupyter notebook
B. a registered model
C. a compute resource
Answer
C. a compute resource
Explanation
The correct answer is C. a compute resource.
To run a pipeline in Azure Machine Learning designer, you need to create a compute resource that will execute the pipeline steps. A compute resource is a cloud-based machine or cluster of machines that you can use to run your machine learning experiments and workflows. You can choose from different types of compute resources, such as Azure Machine Learning Compute, Azure Kubernetes Service, Azure HDInsight, and others.
A Jupyter notebook is a web-based interactive environment that allows you to write and run Python or R code, as well as visualize data and results. You can use Jupyter notebooks to create and run pipelines programmatically, but you do not need them to run pipelines in the designer.
A registered model is a logical container for one or more files that make up your machine learning model. You can register models after you train them, either in the designer or in other tools. Registering models allows you to track and manage them in your workspace, as well as deploy them to various endpoints. However, you do not need to register a model before you can run a pipeline in the designer.
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