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.