> For the complete documentation index, see [llms.txt](https://support.rendered.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://support.rendered.ai/general-concepts/who-uses-rendered.ai.md).

# Who Uses Rendered.ai?

Rendered.ai was designed with two users in mind, who often have separate but overlapping job functions: Synthetic Data Engineers and Computer Vision Engineers.

**Data Scientists and Computer Vision Engineers:** Data Scientists and Computer Vision Engineers approach their business problem with a specific algorithm and learning task in mind. These users focus on how AI can gain a high-level understanding from digital images or video of how the real world behaves. This practitioner is solving a particular AI/ML problem, understands the limitations of the particular algorithm, and designs synthetic data to stretch those limits. 

**Synthetic Data Engineer:** The synthetic data engineer is a practitioner who applies the principles of synthetic data engineering to the design, development, maintenance, testing, and evaluation of synthetic data for consumption by AI/ML algorithms. Competence in this art is obtained through the creation of multiple datasets, with multiple variations, addressing multiple AI learning issues. The experience set tends to be horizontal across multiple engagements and this person has gained domain expertise in profound and nuanced changes on synthetic data and its likely effect on generalized algorithms.

The Platform provides the following capabilities, targeted to each of these users:

| **Benefits for Synthetic Data Engineers**                                                       | **Benefits for Data Scientists and Computer Vision Engineers**                    |
| ----------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------- |
| <ul><li>Secure, collaborative environment</li></ul>                                             | <ul><li>Secure, collaborative environment</li></ul>                               |
| <ul><li>Configuration Management</li></ul>                                                      | <ul><li>No-code dataset generation that is easy to use and master</li></ul>       |
| <ul><li>GPU acceleration, with Compute Management abstracted away</li></ul>                     | <ul><li>Dataset Library Management</li></ul>                                      |
| <ul><li>Easy containerization</li></ul>                                                         | <ul><li>Analytics and comparison tools for datasets</li></ul>                     |
| <ul><li>User friendly web experience for testing configuration and job execution</li></ul>      | <ul><li>Domain matching (Cycle GAN-based)</li></ul>                               |
| <ul><li>Analytic tools to compare two datasets and their AI/ML outcomes</li></ul>               | <ul><li>Analytic tools to compare two datasets and their AI/ML outcomes</li></ul> |
| <ul><li>3D asset generation and management</li></ul>                                            | <ul><li>Automatic, flexible annotation generation</li></ul>                       |
| <ul><li>Example Channel/Application SDK</li></ul>                                               | <ul><li>Rapid “what if” dataset creation</li></ul>                                |
| <ul><li>Cloud-based processing including asynchronous job configuration and execution</li></ul> | <ul><li>Consistency across projects</li></ul>                                     |
| <ul><li>Easily integrated endpoints</li></ul>                                                   | <ul><li><em>Unlimited</em>\* content generation</li></ul>                         |

### Typical Rendered.ai workflows <a href="#typical-rendered.ai-workflows" id="typical-rendered.ai-workflows"></a>

Rendered.ai has been used for some of the following commercial and research applications:

* Generating synthetic CV imagery to train detection algorithms for rare and unusual objects in satellite and aerial imagery
* Generating simulated Synthetic Aperture Radar (SAR) datasets to test and evaluate SAR object detection algorithms
* Embedding synthetic data as an OEM capability underneath a 3rd party x-ray detection system to allow end customers to test domain-specific detection of rare and unusual objects
* Simulating microscopy videos of human oocyte development over time to train AI to recognize different developmental stages

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***

\**Unlimited* refers to the Rendered.ai subscription licensing which allows a set number of users to generate datasets in a managed, hosted compute environment that does not limit the number of jobs run, pixels generated, or images produced.


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