Managing and Securing Your Azure Machine Learning Workspace
After data and access management comes infrastructure. Although Azure Machine Learning is a cloud service, it doesn’t mean that we cannot leverage services together with our Azure or on-premises infrastructure to isolate our resources and secure them from public access.
In this chapter, we will learn how to implement security best practices regarding the workspace. We will focus more on practices and scenarios around virtual networking and endpoint security as well as compute. Compute in Azure Machine Learning can be used both for model training and deployment and each option available has its own security best practices. Compute includes compute instances, compute clusters, and containers. The workspace uses Azure Container Registries to deploy models that can be deployed as containers, so we will review security options for all those services.
In this chapter, we’re going to cover the following main topics...