Added changes from PR #2188 and modified paths & titles

- Changes title of "Data Access" to "Data and Model Access"
- Changes title of "Data Versioning" to "Data and Model Versioning"
- Renames path of Data Access and Data Versioning to
  `data-and-model-access.md` and `data-and-model-versioning.md`
  respectively.
- Adds redirects
-- `/doc/start/data-access` -> `/doc/start/data-and-model-access`
-- `/doc/start/data-versioning` ->
`/doc/start/data-and-model-versioning`
- Replaces links in `/doc/start` with the new links.

content/docs/start/data-access.md → content/docs/start/data-and-model-access.md

@@ -1,13 +1,16 @@
 ---
-title: 'Get Started: Data Access'
+title: 'Get Started: Data and Model Access'
 ---
 
-# Get Started: Data Access
+# Get Started: Data and Model Access
 
-Okay, now that we've learned how to _track_ data and models with DVC and how to
-version them with Git, next question is how can we _use_ these artifacts outside
-of the project? How do I download a model to deploy it? How do I download a
-specific version of a model? How do I reuse datasets across different projects?
+Okay, now that we've learned how to _track_ data files in DVC and how to version
+them with Git. _Models_ in a machine learning project are also files written and
+read by programs and DVC can track and version them similar to data files.
+
+Next question is how can we _use_ these artifacts outside of the project? How do
+I download a model to deploy it? How do I download a specific version of a
+model? How do I reuse datasets across different projects?
 
 > These questions tend to come up when you browse the files that DVC saves to
 > remote storage, e.g.

content/docs/start/data-versioning.md → content/docs/start/data-and-model-versioning.md

@@ -1,6 +1,6 @@
 ---
-title: 'Get Started: Data Versioning'
-description: 'Get started with data versioning in DVC. Learn how to use a
+title: 'Get Started: Data and Model Versioning'
+description: 'Get started with data and model versioning in DVC. Learn how to use a
 regular Git workflow for datasets and ML models, without storing large files in
 Git.'
 ---
@@ -247,6 +247,16 @@ defines data file versions. Git itself provides the version control. DVC in turn
 creates these `.dvc` files, updates them, and synchronizes DVC-tracked data in
 the <abbr>workspace</abbr> efficiently to match them.
 
+## Model versioning
+
+Apart from data files, DVC eases the way you work with models. Models in a
+project usually change more frequently than data files and they need to be kept
+in sync with changes in other elements of a project. Model files are no
+different than data files when it comes to tracking their versions. DVC also
+provides means to track minor changes in model files without fully checking in
+to underlying VCS. In later sections of this series, you'll see how DVC enables
+to track changes in pipelines consisting of multiple model and data files.
+
 ## Large datasets versioning
 
 In cases where you process very large datasets, you need an efficient mechanism

content/docs/start/data-pipelines.md

@@ -143,7 +143,7 @@ stages:
 There's no need to use `dvc add` for DVC to track stage outputs (`data/prepared`
 in this case); `dvc run` already took care of this. You only need to run
 `dvc push` if you want to save them to
-[remote storage](/doc/tutorials/get-started/data-versioning#storing-and-sharing),
+[remote storage](/doc/start/data-and-model-versioning#storing-and-sharing),
 (usually along with `git commit` to version `dvc.yaml` itself).
 
 ## Dependency graphs (DAGs)

content/docs/start/index.md

@@ -53,15 +53,16 @@ Now you're ready to DVC!
 DVC's features can be grouped into functional components. We'll explore them one
 by one in the next few pages:
 
-- [**Data versioning**](/doc/start/data-versioning) (try this next) is the base
-  layer of DVC for large files, datasets, and machine learning models. Use a
-  regular Git workflow, but without storing large files in the repo (think "Git
-  for data"). Data is stored separately, which allows for efficient sharing.
-
-- [**Data access**](/doc/start/data-access) shows how to use data artifacts from
-  outside of the project and how to import data artifacts from another DVC
-  project. This can help to download a specific version of an ML model to a
-  deployment server or import a model to another project.
+- [**Data and model versioning**](/doc/start/data-and-model-versioning) (try
+  this next) is the base layer of DVC for large files, datasets, and machine
+  learning models. Use a regular Git workflow, but without storing large files
+  in the repo (think "Git for data"). Data is stored separately, which allows
+  for efficient sharing.
+
+- [**Data and model access**](/doc/start/data-and-model-access) shows how to use
+  data artifacts from outside of the project and how to import data artifacts
+  from another DVC project. This can help to download a specific version of an
+  ML model to a deployment server or import a model to another project.
 
 - [**Data pipelines**](/doc/start/data-pipelines) describe how models and other
   data artifacts are built, and provide an efficient way to reproduce them.

redirects-list.json

@@ -26,6 +26,8 @@
   "^/doc/tutorials/get-started(/.*)?$                                                     /doc/start",
   "^/doc/tutorials/versioning(/.*)?$                                                      /doc/use-cases/versioning-data-and-model-files/tutorial",
   "^/doc/tutorials(/.*)?                                                                  /doc/start",
+  "^/doc/start/data-versioning(/.*)?$                                                     /doc/start/data-and-model-versioning",
+  "^/doc/start/data-access(/.*)?$                                                         /doc/start/data-and-model-access",
 
   "^/doc/use-cases/data-and-model-files-versioning/?$                                     /doc/use-cases/versioning-data-and-model-files",
   "^/doc/user-guide/updating-tracked-files$                                               /doc/user-guide/how-to/update-tracked-data",