> For the complete documentation index, see [llms.txt](https://ai-docs.fptcloud.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://ai-docs.fptcloud.com/fpt-ai-studio/before-you-start/architecture.md).

# Architecture

AI Studio provides an integrated platform that covers the entire lifecycle of AI model development — from data preparation and fine-tuning to testing, deployment, and management. The platform is designed to help developers, researchers, and enterprises efficiently build, optimize, and operate AI models at scale.

### Components

The platform is built around five main components:

| Component             | Description                                                                                                                                           |
| --------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Model Hub**         | Central repository for storing, versioning, and deploying models. It ensures consistency and accessibility across teams and environments.             |
| **Model Fine-tuning** | A managed service that enables users to train or adapt existing pretrained models to their specific datasets. Supports scalable distributed training. |
| **Model Testing**     | Provides tools and environments to validate model performance and compare results across model versions before deployment.                            |
| **Data Hub**          | Secure and scalable data management service. Handles dataset upload, organization, and linkage with fine-tuning and testing jobs.                     |
| **User Token**        | Identity and access management system. Used for authentication, permission control, and API integrations.                                             |

### How Components Work Together

1. Users upload and manage datasets in **Data Hub**.
2. They fine-tune models using **Model Fine-tuning**, referencing datasets from Data Hub.
3. Fine-tuned models are stored, versioned, and deployed via **Model Hub**.
4. Performance is validated through **Model Testing**.
5. Access and automation are managed securely with **User Tokens**.

This modular yet interconnected architecture helps you move seamlessly from raw data to production-grade AI models within one unified environment.


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