- Lab
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Libraries: If you want this lab, consider one of these libraries.
- AI
- Cloud
Create a Machine Learning Workspace in Azure
Where do you start when migrating your machine learning workflows to Azure? How do you get the necessary resources provisioned? What resources will you even need? Azure Machine Learning workspaces help answer all of these questions. This lab will have you create an Azure Machine Learning workspace, which is the first step in doing machine learning on Azure. ## Scenario As your team migrates to the cloud, you need to provision Azure resources to allow you to train machine learning models. You'll need Storage and Compute, a way to monitor your progress, and a place to work on your Notebooks. Azure provides all of these in one place: Machine Learning workspaces. You will practice setting up a Machine Learning Studio workspace for future use.
Lab Info
Table of Contents
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Challenge
Provision the Machine Learning Workspace
- Create a Machine Learning Studio workspace. Look at the Infrastructure-as-Code template for the workspace, so you can begin to automate this process.
- View the Resource Group to see all resources created.
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Challenge
Create a Notebook to Run Code
- Create a new Compute Instance to host your Notebook. The Virtual Machine will only be used for the Notebook, so use the lowest power, "general purpose" machine from among the recommended options.
- Create a new Python Notebook using a version that includes the Python AzureML kernel.
- Print
Hello, world!in the notebook to verify that it is working.
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Challenge
Stop the Notebook Server
Stop the Compute Instance being used to run the Jupyter Notebook.
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