Summary

In this chapter, you learned about the prerequisites for deploying Azure ML. You can work with either a trial subscription or you can request a resource group in your company's subscription, where you, at the very least, have contributor rights to the resource group. You also discovered, in depth, the two most common ways of deploying the Azure ML workspace in a development environment, and you also gained an understanding of the parameters that you need to specify. You also learned about the alternative ways you can deploy the workspace, including ARM templates, which are the more DevOps-friendly way of deploying in production environments. In the final section, you looked at the resources that are part of Azure ML workspace deployment, and you learned how RBAC works in Azure. Additionally, you learned how to use built-in or custom roles to give access to the Azure ML workspace you deployed.

In the next chapter, you will learn about the Azure ML Studio experience,...