> For the complete documentation index, see [llms.txt](https://docs.datasaur.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.datasaur.ai/build-model/datasaur-dinamic.md).

# Datasaur Dinamic

## Introduction

**Datasaur Dinamic** lets you train and deploy models directly from your labeled data. It integrates with Amazon SageMaker and Hugging Face AutoTrain to support automated model training workflows.

<figure><img src="/files/dS9VNn0Vd2AIb6Ui6vah" alt=""><figcaption></figcaption></figure>

## Key features

**Datasaur Dinamic** provides several benefits:

* **Automated model training**: Streamline the process of training machine learning models using labeled data.
* **Efficient labeling**: Use the trained models for **ML-assisted labeling**, improving the efficiency and accuracy of future labeling tasks.
* **Integration with applications**: Deploy the trained models directly into applications, enhancing functionality with precise data insights.

## Supported service providers

{% hint style="info" %}
To use **Datasaur Dinamic** effectively, ensure the dataset contains at least 10 labeled rows or sentences.
{% endhint %}

| Service providers                                           | Supported project types     | Configurations                                                                   |
| ----------------------------------------------------------- | --------------------------- | -------------------------------------------------------------------------------- |
| [Amazon SageMaker](https://aws.amazon.com/sagemaker/train/) | Row labeling                | [Amazon SageMaker Configuration](/build-model/datasaur-dinamic/aws-sagemaker.md) |
| [Hugging Face Auto Train](https://huggingface.co/autotrain) | Row labeling, Span labeling | [Hugging Face Configuration](/build-model/datasaur-dinamic/hugging-face.md)      |


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