This epsisode of the AI Show talks about the new ML assisted data labeling capability in Azure Machine Learning 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.

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Here’s an interesting look at “Data Labelers” and how cheap human labor could once again be China’s competitive advantage.

In order to avoid plowing into other cars or making illegal lane changes, they need a lot of help. In China, that help is increasingly coming from rooms full of college students. Li Zhenwei is a data labeler. His job, which didn’t even exist a few years ago, involves sitting at a computer, clicking frame-by-frame through endless hours of dashcam footage, and drawing lines over each photo to help the computer recognize lane markers.