# Labeling

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Labeling - Data Management
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## Summary

* Data labeling requires a collection of data points such as images, text, or audio and a qualified team of people to label each of the input points with meaningful information that will be used to train a machine learning model.
* You can create a **user interface** with a standard set of features (bounding boxes, segmentation, key points, cuboids, set of applicable classes…) and train your own annotators to label the data.
* You can leverage other labor sources by either **hiring** your own annotators or **crowdsourcing** the annotators.
* You can also consult standalone **service companies**. Data labeling requires separate software stack, temporary labor, and quality assurance; so it makes sense to **outsource**.