Currently Azure Lab Services enables you to set up one template virtual machine in a lab and make a single copy available to each of your user. But if you are a professor teaching an IT class on how to set up firewalls or servers, you may need to provide each of your students with an environment in which multiple virtual machines can talk to each other over a network.
Nested virtualization enables you to create a multi-VM environment inside a lab’s template virtual machine. Publishing the template will provide each user in the lab with a virtual machine set up with multiple VMs within it.
The Azure DevTest Lab service significantly improves management of virtual machines for development and testing activities. However, team or infrastructure requirements change over time and we need to adapt our current activities to meet the new requirements – to enable this adaptation we’re introducing the ability to import Virtual Machines to another lab!
One of the great benefits of Azure virtual machines is the ability to change the size of your virtual machine based on the needs for CPU, network or disk performance. Lab users can now leverage this feature inside DevTest Labs. This feature allows users to update the size of their virtual machines according to their needs without having to create a fresh machine every time their requirements change.
The resize feature will respect the lab policy for allowed virtual machine sizes within the lab. This means as a lab user you will be able to resize your machines to one of the available sizes in your lab.
We are excited to announce that Azure DevTest Labs is being expanded to offer new types of labs! These new lab types are managed labs, for which the service handles all the Azure infrastructure management for you. The managed lab types have entered Public Preview, and once the preview ends, the new lab types and the existing DevTest Labs will come under a new common umbrella name of Azure Lab Services.
Newly available as a Cognitive Services lab, Project Conversation Learner enables you to build and teach conversational interfaces that learn directly from example interactions. Spanning a broad set of task-oriented use cases, Project Conversation Learner applies machine learning behind-the-scenes to decrease manual coding of dialogue control logic. The Project Conversation Learner SDK works in conjunction with the Bot Builder SDK v4 preview, which makes bots, skills, and other conversational UIs built with Project Conversation Learner easy to deploy via the Bot Framework.