Labeling (3) - Data Management - Full Stack Deep Learning

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--------------------------------------------------------------------------------------------- What are effective ways to label your data?

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.

[donalmclaughlin]

Awesome content! Humanloop out of London is the next gen of annotation interfaces, especially if you don’t want or can’t outsource your project.

[okayokay]

Finding data labelling companies! Anybody here?