> For the complete documentation index, see [llms.txt](https://fall2019.fullstackdeeplearning.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://fall2019.fullstackdeeplearning.com/course-content/data-management/labeling.md). # 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 This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## 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, and the optional `goal` query parameter: ``` GET https://fall2019.fullstackdeeplearning.com/course-content/data-management/labeling.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. 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.