# Labeling {% embed url="" %} Labeling - Data Management {% endembed %} ## 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**. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://fall2019.fullstackdeeplearning.com/course-content/data-management/labeling.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.