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

# Distributed Data Ecosystem

Alaya AI utilises a distributed data sampling structure that connects global data provider communities through an integrated Web3 data infrastructure network. Alaya AI’s standard data business process follows a streamlined and highly automated sequence:

* AI training questions are provided by data customers and added to Alaya’s question bank based on each customer’s specific data requirements.
* Alaya AI automatically distributes training tasks from our extensive AI/ML question bank to suitable users for labelling and annotation based on user records and expertise (e.g., verification rates in each area of expertise).
* Training tasks are completed by a distributed network of individual contributors through targeted sampling algorithms optimised through HITL-assisted AI model fine-tuning.
* Automated data preprocessing and quality assessment is applied before final delivery based on customer data specifications. ZK-encryption and data desensitisation are applied to ensure minimal privacy risks for data contributors.

Data quality is verified by preprocessing algorithms on Alaya AI’s Optimisation Layer through Gaussian approximation and particle swarm optimisation, while sampling bias is minimised through Alaya AI’s large and diverse user communities.


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