# Pick your starting point

Pick the setup that fits your work. Both paths lead to the same engine, with
a first flow to try along the way.

<div class="fluksio-lanes" markdown>

<div class="fluksio-lane fluksio-lane--science" markdown>

### Data science

*"I have some Python code. I want to keep track of what I ran."*

Install Fluksio with pip in your research environment. Turn a function into a
node, record metrics and artifacts, and compare experiments. Connect to
[fluksio.com](https://fluksio.com) for the browser dashboard.

[Start with Python →](data-science.md)

</div>

<div class="fluksio-lane fluksio-lane--facility" markdown>

### Facility automation

*"I have a bunch of sensors. I want them to do something."*

Start one Docker container for a local engine and browser interface, on a
server, a PC or a Raspberry Pi. Build a live flow, connect your devices, and
put their values on a dashboard.

[Start with Docker →](facility-automation.md)

</div>

</div>

## Next steps

- [Flows, nodes and messages](../concepts/flows.md) explains the graph.
- [The flow editor](../interface/flow-editor.md) covers editing and publishing.
- [The command line](../code/cli.md) covers local and remote use.
- [Configuration](../reference/configuration.md) lists settings and data paths.