Make documentation links usable from repository previews

docs/how-to-guides/call-remote.md

@@ -22,7 +22,7 @@ async def slow_greeter(your_name):
     await sleep(2)
     return f'Hi {your_name}!'
 ```
-> Run this in development mode by executing `great-ai server.py` or `python3 -m great_ai server.py` if you're on Windows and [`great-ai` is not in your `PATH`](/how-to-guides/install).
+> Run this in development mode by executing `great-ai server.py` or `python3 -m great_ai server.py` if you're on Windows and [`great-ai` is not in your `PATH`](https://great-ai.scoutinscience.com/how-to-guides/install).
 
 ### Client
 
@@ -48,7 +48,7 @@ print(results)
 
 > Run this script as a regular Python script by executing `python3 client.py`.
 
-![screenshot of result](/media/remote-sync.png){ loading=lazy }
+![screenshot of result](https://great-ai.scoutinscience.com/media/remote-sync.png){ loading=lazy }
 
 As you can see, everything worked as expected. There is one way to improve it, though.
 ## An `async` example
@@ -86,6 +86,6 @@ asyncio.run(main())
 
 > Replace `client.py` with this async client. Note that although async support is significantly more streamlined in recent Python versions, it still requires a bit more boilerplate than its synchronous counterpart.
 
-![screenshot of result](/media/remote-async.png){ loading=lazy }
+![screenshot of result](https://great-ai.scoutinscience.com/media/remote-async.png){ loading=lazy }
 
 This also works and might be considerably quicker in some use cases.

docs/how-to-guides/create-service.md

@@ -38,7 +38,7 @@ This not only allows you to statically type-check your code, but by default, Gre
 
 ## With async
 
-Asynchronous code can result in immense performance gains in some instances. For example, you might rely on a third-party service, do database access, or [call a remote GreatAI instance](/how-to-guides/call-remote). In these cases, you can make your function `async` without any other changes.
+Asynchronous code can result in immense performance gains in some instances. For example, you might rely on a third-party service, do database access, or [call a remote GreatAI instance](https://great-ai.scoutinscience.com/how-to-guides/call-remote). In these cases, you can make your function `async` without any other changes.
 
 ```python title="async_greeter.py"
 from great_ai import GreatAI
@@ -117,4 +117,4 @@ def add_number(positive_number: int, secret: int) -> int:
 assert add_number(1).output == 5
 ```
 
-1. Refer to [the configuration page](/how-to-guides/configure-service) for specifying where to store your models. 
+1. Refer to [the configuration page](https://great-ai.scoutinscience.com/how-to-guides/configure-service) for specifying where to store your models.

docs/how-to-guides/handle-training-data.md

@@ -60,7 +60,7 @@ After the initial data gathering, end-to-end feedback can also be integrated int
 
 The scaffolded REST API contains endpoints for managing traces and their feedbacks.
 
-![screenshot of swagger](/media/feedback.png){ loading=lazy }
+![screenshot of swagger](https://great-ai.scoutinscience.com/media/feedback.png){ loading=lazy }
 
 When [great_ai.query_ground_truth][] is executed, it implicitly filters for traces that have feedback. Therefore, both the `ground_truth` and the `online` traces that have received feedback are returned. No matter the origin of the data, it can be accessed using the same API.
 

docs/how-to-guides/large-file.md

@@ -1,6 +1,6 @@
 # How to use LargeFiles
 
-The functions [save_model][great_ai.use_model] and [@use_model][great_ai.use_model] wrap LargeFile instances. Hence, besides configuring [LargeFile](/reference/large-file), users have few reasons to use LargeFiles directly.
+The functions [save_model][great_ai.use_model] and [@use_model][great_ai.use_model] wrap LargeFile instances. Hence, besides configuring [LargeFile](https://great-ai.scoutinscience.com/reference/large-file), users have few reasons to use LargeFiles directly.
 
 ## Motivation
 

docs/how-to-guides/use-service.md

@@ -1,6 +1,6 @@
 # How to perform prediction with GreatAI
 
-After [creating a GreatAI service](/how-to-guides/create-service) by wrapping your prediction function, and optionally [configuring it](/how-to-guides/configure-service), it's time to do some prediction.
+After [creating a GreatAI service](https://great-ai.scoutinscience.com/how-to-guides/create-service) by wrapping your prediction function, and optionally [configuring it](https://great-ai.scoutinscience.com/how-to-guides/configure-service), it's time to do some prediction.
 
 Let's take the following example: