changed link | intext links to footer

- PR partly (1 of 3) fixes aicoe-aiops#12
  - MUST: 1 of 3 incorrect links changed (relativ to absolut path) (c)
  - NTH: changed all intext links to references in footer

docs/get-started.md

@@ -5,14 +5,21 @@ This project is made to walk users through what model serving is, and how it fit
 
 ## Launch Project and Run Notebooks via JupyterHub
 
-To make the notebooks reproducible, we have deployed containerized notebook images on the public [JupyterHub](https://jupyterhub-opf-jupyterhub.apps.zero.massopen.cloud) instance on the [Massachusetts Open Cloud](https://massopen.cloud/). You can get access to a Jupyter environment using your Google account! To do so, please follow the steps below:
+To make the notebooks reproducible, we have deployed containerized notebook images on the public [JupyterHub][jupyterhub] instance on the [Massachusetts Open Cloud][moc]. You can get access to a Jupyter environment using your Google account! To do so, please follow the steps below:
 
-1. Visit the Operate First [JupyterHub](https://jupyterhub-opf-jupyterhub.apps.zero.massopen.cloud)
+1. Visit the Operate First [JupyterHub][jupyterhub]
 2. Click on `Log in with moc-sso` and sign in through Google.
 3. On the spawner page, select `Image Detection` for notebook image, `Large` for container size, and then click `Start server` to spawn your server.
 4. Once your server has spawned, you should see a directory titled `pet-image-detection-<current-timestamp>`. All the notebooks should be available inside the `notebooks` directory in it for you to explore.
 
 
 ## More resources
 
-If you are looking for more, a version of this demo was presented at [DevConf.CZ](https://www.devconf.info/cz/) March 2021, "Beyond Inference: Bringing ML into Production." The video is available [here](https://www.youtube.com/watch?v=3ng-WcN_Th8) and slides avaiable [here](../slides). This talk explains the basics of model serving, why this is a relevant issue, how model serving offers relief for the data scientist/software engineer handoff, and know how to deploy a machine learning model with Seldon Core.
+If you are looking for more, a version of this demo was presented at [DevConf.CZ][devconfcz] March 2021, "Beyond Inference: Bringing ML into Production." The video is available [here][video] and slides avaiable [here][slides]. This talk explains the basics of model serving, why this is a relevant issue, how model serving offers relief for the data scientist/software engineer handoff, and know how to deploy a machine learning model with Seldon Core.
+
+
+[jupyterhub]: https://jupyterhub-opf-jupyterhub.apps.zero.massopen.cloud
+[moc]: https://massopen.cloud/
+[devconfcz]: https://www.devconf.info/cz/
+[video]: https://www.youtube.com/watch?v=3ng-WcN_Th8
+[slides]: https://github.com/aicoe-aiops/pet-image-detection/tree/main/slides