Fix typos, and other small changes in the Model Management Get Starte…

…d pages (#5000)

* Fix typos, and other small changes

* Remove id from admon

content/docs/start/model-management/model-cicd.md

@@ -17,21 +17,21 @@ If you are using the example repository, the models are already versioned on a
 publicly readable DVC remote so you can access the model from there and use it.
 If you are instead using your own repository you need to set up your own DVC
 remote and push the data (including models) there. Have a look at our
-[Data management guide](/doc/start/data-management/data-versioning#configuring-a-remote)
+[Data Management guide](/doc/start/data-management/data-versioning#configuring-a-remote)
 to see how this is done.
 
 ## Downloading models
 
 It is useful to download model artifacts for example for local testing or for
 use in CICD workflows. With models versioned by DVC this can be done easily by
-using the Studio UI.
+using the DVC Studio UI.
 
 Go to the detailed view of your model, select the desired model version under
 the "Version info" and then click on the "Access model" button.
 
-Studio will present you with several ways of downloading models - with the CLI,
-in Python code and directly from your web browser. You can see all the web
-browser download steps here:
+DVC Studio will present you with several ways of downloading models - with the
+CLI, in Python code and directly from your web browser. You can see all the
+steps to download the model from the web browser here:
 
 <video width="99%" height="540" autoplay loop muted>
     <source src="/img/mr-studio-download-model.webm" type="video/webm">
@@ -54,21 +54,22 @@ $ dvc artifacts get https://github.com/<user>/example-get-started-experiments po
 ```
 
 Here you just need to replace `<user>` with your GitHub user. This will download
-the latest version of the `pool-segmentation` model from the DVC Remote
+the latest version of the `pool-segmentation` model from the DVC remote
 associated with the Git repository in the URL. You can also specify a different
 artifact version or a model registry stage. See the `dvc artifacts get`
 documentation for all options.
 
-If you don't have a Studio account at all, you can still use `dvc artifacts get`
-to download models, but you will need to provide the correct Git and DVC Remote
-credentials manually. You can see more details in the
+If you don't have a DVC Studio account at all, you can still use
+`dvc artifacts get` to download models, but you will need to provide the correct
+Git and DVC remote credentials manually. You can see more details in the
 [documentation](/doc/command-reference/artifacts/get#description).
 
 ## Connecting model registry actions to your CICD
 
-As we [noted](/docs/start/model-management/model-registry#GTO-tip) in the model
-registry chapter, all DVC model registry actions are captured in your Git
-repository as Git tags with a specific format.
+As we noted in the
+[model registry chapter](/doc/start/model-management/model-registry), all DVC
+model registry actions are captured in your Git repository as Git tags with a
+specific format.
 
 This also means that we can create CICD actions in our Git repository which will
 be triggered whenever versions are registered or stages are assigned.
@@ -176,17 +177,17 @@ GitHub repository as a
 [secret](https://docs.github.com/en/actions/security-guides/using-secrets-in-github-actions)
 to manage authentication with the
 [DVC remote storage](https://dvc.org/doc/user-guide/data-management/remote-storage#remote-storage).
-This way we only need to keep the Studio token saved on GitHub and let Studio
-manage the specific storage credentials for us.
+This way we only need to keep the DVC Studio token saved on GitHub and let
+Studio manage the specific storage credentials for us.
 
 Finally, `github.server_url` and `github.repository` are
 [default environmental variables in GitHub](https://docs.github.com/en/actions/learn-github-actions/contexts#github-context)
 which together form the URL of our repository on GitHub. We could of course also
 specify the URL manually.
 
-If you don't use Studio, you can still use `dvc artifacts get` but you will need
-to keep your remote storage credentials on GitHub and use them to configure DVC
-in the CICD workflow. You will also need to checkout the repository in the
+If you don't use DVC Studio, you can still use `dvc artifacts get` but you will
+need to keep your remote storage credentials on GitHub and use them to configure
+DVC in the CICD workflow. You will also need to checkout the repository in the
 workflow. You can see more details in the
 [documentation](/doc/command-reference/artifacts/get#description).
 

content/docs/start/model-management/model-registry.md

@@ -14,10 +14,10 @@ up a model registry where we can discover, share, deploy and audit all our
 models and which will serve as the single source of truth for our model
 management.
 
-<admon type="tip" id="GTO-tip">
+<admon type="tip">
 
-Behind the scenes, DVC Studio uses a command line tool called
-[GTO](<(/doc/gto)>) for most model registry actions.
+Behind the scenes, DVC Studio uses a command line tool called [GTO](/doc/gto)
+for most model registry actions.
 
 With GTO you can also set up the model registry locally without DVC Studio. You
 can see how this is done in the expandable "Under the hood" sections in this
@@ -37,7 +37,7 @@ to set it up locally.
 
 To then perform the model registry actions in this guide, follow
 [these steps](/doc/studio/user-guide/experiments/create-a-project#connect-to-a-git-repository-and-add-a-project)
-to sign in to DVC Studio, connect it to your GitHub account and add your forked
+to sign in to DVC Studio, connect it to your Git account and add your forked
 repository as a DVC Studio project.
 
 </details>
@@ -102,12 +102,12 @@ registry.
 
 Now we just need to run the Python script which includes this code to cache and
 register the model. If you are
-[following](/docs/start/model-management/model-registry#follow-along-instructions)
+[following](/doc/start/model-management/model-registry#follow-along-instructions)
 our example repository then this has already been done and we can continue to
 the next section.
 
 If you are building your own repository, you will need to run the script and
-push the result to your Git remote (GitHub) yourself.
+push the result to your Git remote (e.g., GitHub) yourself.
 
 <details id="push-click-to-see-other-ways-to-add-models">
 
@@ -163,9 +163,9 @@ in your Git repository!
 It also allows you to use GTO directly instead of the DVC Studio UI to manage
 your model lifecycle. That can be useful for example if you want to trigger
 certain model registry actions programmatically. You can learn more about the
-details of GTO in its [documentation](/docs/gto).
+details of GTO in its [documentation](/doc/gto).
 
-If you don't have a Studio account at all, you will have to manage a model
+If you don't have a DVC Studio account at all, you will have to manage a model
 registry separately for each Git repository however.
 
 </details>
@@ -253,7 +253,7 @@ and denote that it is no longer in the "dev" stage. First, assign the model
 version to the "prod" stage just like we did with the "dev" stage in the
 previous section.
 
-Now, to remove the "dev" stage from our model version 1.0.0 and it assigned only
+Now, to remove the "dev" stage from our model version 1.0.0 and assign it only
 to "prod", follow these steps:
 
 <video width="99%" height="540" autoplay loop muted>
@@ -265,7 +265,7 @@ from the registry entirely. To see how, have a look at the
 [documentation](/doc/studio/user-guide/model-registry/remove-a-model-or-its-details).
 
 The detailed view of our model in the registry should now match what we see
-[in our example](<(https://studio.iterative.ai/team/Iterative/models/b3P4bcYIrGYdzyjqzsf9Xw==/pool-segmentation/v1.0.0)>).
+[in our example](https://studio.iterative.ai/team/Iterative/models/b3P4bcYIrGYdzyjqzsf9Xw==/pool-segmentation/v1.0.0).
 
 <details id="under-the-hood-removing-stages">
 
@@ -287,7 +287,7 @@ you should see something like this:
 ![Model history](/img/mr-model-history.png)
 
 As we noted
-[above](/docs/start/model-management/model-registry#under-the-hood-model-registry),
+[above](/doc/start/model-management/model-registry#under-the-hood-model-registry),
 DVC uses special Git tags to keep track of model registry actions, so all of
 this history is actually stored directly in your Git repository. DVC Studio can
 parse these tags and show them to us in a user-friendly way.