> ## Documentation Index
> Fetch the complete documentation index at: https://mage-staging.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Features

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  <img class="hero inline" alt="" src="https://user-images.githubusercontent.com/78053898/198751496-22d51f03-f09f-454c-8f98-409b5dbec8f9.svg" />
</Frame>

<CardGroup>
  <Card title="Data pipeline management" icon="diagram-project" href="/design/data-pipeline-management" />

  <Card title="Notebook for building data pipelines" icon="notebook" href="/about/features#1-data-centric-editor" />

  <Card title="Changelog" icon="list-check" href="https://github.com/mage-ai/mage-ai/releases" />

  <Card title="Roadmap" icon="map" href="https://airtable.com/shrJS0cDOmQywb8vp" />
</CardGroup>

## Data pipeline management

👉 See more
[details here](/design/data-pipeline-management).

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  <img alt="" src="https://github.com/mage-ai/assets/blob/main/orchestration-overview.gif?raw=true" />
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## Notebook for building data pipelines

### 1. Data centric editor

An interactive coding experience designed for preparing data to train ML models.

Visualize the impact of your code every time you load, clean, and transform
data.

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  <img alt="Data centric editor" src="https://github.com/mage-ai/assets/blob/main/data-centric-editor.png?raw=True" />
</Frame>

### 2. Production ready code

No more writing throw away code or trying to turn notebooks into scripts.

Each block (aka cell) in this editor is a modular file that can be tested,
reused, and chained together to create an executable data pipeline locally or in
any environment.

Read more about [blocks](/design/blocks) and how they work.

<Frame>
  <img alt="Production ready code" src="https://github.com/mage-ai/assets/blob/main/data-pipeline.png?raw=True" />
</Frame>

Run your data pipeline end-to-end using the command line function:
`$ mage run [project] [pipeline]`

You can run your pipeline in production environments with the orchestration
tools

* [Airflow](/guides/integrate-mage-airflow)

* [Prefect](/integrations/prefect)

### 3. Extensible

Easily add new functionality directly in the source code or through plug-ins
(coming soon).

Adding new API endpoints ([Tornado](https://www.tornadoweb.org/en/stable/)),
transformations (Python, PySpark, SQL), and charts (using
[React](https://reactjs.org/)) is easy to do (tutorial coming soon).

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  <img alt="Extensible charts" src="https://github.com/mage-ai/assets/blob/main/extensible-charts.gif?raw=True" />
</Frame>