> For the complete documentation index, see [llms.txt](https://labelfi-1.gitbook.io/labelfi/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://labelfi-1.gitbook.io/labelfi/basics/appendix/11.1-glossary-of-terms.md).

# 11.1 Glossary of Terms

* AI-Enabled Labeling: Use of artificial intelligence to assist in automating parts of the data labeling process, improving speed and accuracy.
* Annotation: The process of labeling or categorizing data, such as images, text, or video, to make it usable for machine learning models.
* Blockchain: A decentralized and distributed digital ledger used for recording transactions in a way that is secure and transparent \[5].
* DAO (Decentralized Autonomous Organization): A blockchain-based organization governed by smart contracts, allowing decisions to be made collectively by members \[26].
* Data labeling Marketplace: A platform where individuals or companies can offer and perform data-labeling tasks for machine learning.
* Decentralized Storage: A method for storing data across multiple nodes, increasing security and reducing the risks of a single point of failure. Examples include Filecoin and Arweave \[5]\[12].
* GPU Computing: The use of Graphics Processing Units (GPUs) for data processing, significantly improving the speed of tasks such as data labeling.
* LBF (Labelfi Token): The native cryptocurrency of the Labelfi platform, used for rewards, payments, and staking \[26].
* MLOps: A set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently \[12].
* PoW (Proof of Work): A consensus mechanism where participants perform computations (work) to validate blockchain transactions \[5].
* PoS (Proof of Stake): A consensus mechanism where participants lock up tokens as collateral to validate transactions on the blockchain \[5]\[12].
* SDK (Software Development Kit): A set of tools and libraries that allow developers to integrate Labelfi’s features into their applications \[12].
* Supernode: In Labelfi, supernodes are selected participants that validate data labeling tasks and can influence platform governance through staking \[26].
* T2E (Train to Earn): A model in which users earn rewards by completing data-labeling tasks, with rewards being distributed based on task quality and completion \[26].