Data Labeling in Azure ML Studio

You can create a data labeling project and either label the data yourself, or take help of other domain experts to create labels for you. Multiple labelers can use browser based labeling tools and work in parallel. As human labelers create labels, an ML model is trained in the background and its output is used to accelerate the data labeling workflow in various ways such as active learning, task clustering, and pre-labeling. Finally, you can export the labels in different formats.

For more tips like this, check out the working remotely playlist at www.youtube.com/FoetronAcademy. 

Also, if you need any further assistance then you can raise a support ticket (https://cloud.foetron.com/) and get it addressed.