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

# Features

![](https://user-images.githubusercontent.com/78053898/198751496-22d51f03-f09f-454c-8f98-409b5dbec8f9.svg)

<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).

![](https://mage-ai.github.io/assets/orchestration-overview.gif)

## 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.

<img alt="Data centric editor" src="https://mage-ai.github.io/assets/data-centric-editor.png" />

### 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.

<img alt="Production ready code" src="https://mage-ai.github.io/assets/data-pipeline.png" />

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).

<img alt="Extensible charts" src="https://mage-ai.github.io/assets/extensible-charts.gif" />