> For the complete documentation index, see [llms.txt](https://label.gitbook.io/documentation/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://label.gitbook.io/documentation/about-label/about-label.md).

# About Label

Data labeling is a crucial step in the development of machine learning models, as it involves annotating raw data to make it meaningful for AI applications. \
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However, the process of data labeling can be time-consuming and resource-intensive. To address this challenge, Label was started to solve this critical bottleneck through a global and decentralized approach. \
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Label wants to accelerate AI development, by providing the necessary tools for labeling different types of data, such as images, text, videos, and audio. This approach promotes an iterative process, allowing everyone to label data in smaller batches and prioritize the quality of the labeled data. \
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By fostering two-way collaboration between labelers and AI companies, this approach can result in a significant reduction in the amount of training data needed, leading to time and cost savings during the data labeling process.\
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Another important development is the use of a decentralized economy, which involves training a machine learning model to label data automatically. This approach can help save time and resources, as the system can immediately start the labeling process without the need for a large in-house team. \
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By combining machine learning developments with human-in-the-loop participation, a global data labeling can streamline the data labeling process and improve efficiency. Overall, Label aims at addressing the challenges associated with data labeling for AI development, ultimately contributing to the creation of high-quality training datasets for machine learning models.