Merge pull request #57 from aicoe-aiops/Shreyanand-patch-1

Update links for the migrated Jupyterhub

Author: sesheta

docs/get-started.md

@@ -4,9 +4,9 @@ This project discusses how we can manipulate, visualize, and learn from the time
 
 ## Try it out yourself
 
-There are interactive and reproducible notebooks for this entire [project](https://github.com/aicoe-aiops/time-series) available for anyone to start using on the public [JupyterHub](https://jupyterhub-opf-jupyterhub.apps.zero.massopen.cloud/hub/login) instance on the [Massachusetts Open Cloud](https://massopen.cloud/) (MOC) right now!
+There are interactive and reproducible notebooks for this entire [project](https://github.com/aicoe-aiops/time-series) available for anyone to start using on the public [JupyterHub](https://jupyterhub-opf-jupyterhub.apps.smaug.na.operate-first.cloud/) instance on the [Massachusetts Open Cloud](https://massopen.cloud/) (MOC) right now!
 
-1. To get started, access [JupyterHub](https://jupyterhub-opf-jupyterhub.apps.zero.massopen.cloud/), select log in with `moc-sso` and sign in using your Google Account.
+1. To get started, access [JupyterHub](https://jupyterhub-opf-jupyterhub.apps.smaug.na.operate-first.cloud/), select log in with `operate-first` and sign in using your Google Account.
 2. After signing in, on the spawner page, please select the `Time Series Notebook Image` in the JupyterHub Notebook Image section from the dropdown and select a `Medium` container size and hit `Start` to start your server.
 3. Once your server has spawned, you should see a directory titled `s2i-time-series-<current-timestamp>`.
 4. Go to `notebooks` and run the notebook you want to experiment with.