Labeling

What are effective ways to label your data?
Labeling - Data Management

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.