Preparing the Azure Machine Learning Workspace (Part 1 of 2)

By Ashwin Venugopal -

 





To read part 2, please click here




Deploying an Azure Machine Learning Workspace

Firstly, we will require an Azure subscription so that you can log-in to the Azure portal with your identity and with the knowledge of the Azure subscription to which you would like to deploy your ML services.

Now, if you want to use your work account, then, you will have to go to porta.azure.com in order to log-in. If it works, then, this means that your company has already set up an Azure AD instance. After this, you can talk to your Azure Global Administrator to discuss about the Azure subscription to use for your purpose.

However, if you want to use your private account, then, go to azure.com and click on Free Account to create an Azure AD for yourself with a free trial subscription containing a certain amount of money to spend within 30 days on Azure services.

Understanding the Available Tooling for Azure Deployments

In Azure, any action that deploys or change an Azure service goes through ARM (Azure Resource Manager) which accepts requests either from the Azure portal, Azure PowerShell, the Azure CLI, or the Azure REST API. Some of them are explained below:

  • Azure CLI- This fully-fledged command-line environment can be installed in any major operating system and its latest version can be downloaded from https://docs.microsoft.com/en-us/cli/azure/ install-azure-cli.

  • Azure PowerShell- It's a library of PowerShell modules that can be added to a PowerShell environment and the new PowerShell Core 7.x can also support all the major Linux releases and macOS (before this, it was only available on Windows).

  • Azure REST API- This one is available to call ARM through REST, allowing you to manage Azure resources via curl or the popular Python requests library. 

All the above above options not only allow the use of so-called ARM templates, Azures version of Infrastructure as Code (IaC); but also offers you the ability to save as well as version-control the infrastructure definitions in files which is highly recommended while dealing with the complex ones.  









To read part 2, please click here







































































































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