start: Data Access and Data Versioning to mention Model in titles (#2096)

content/blog/2020-10-12-october-20-dvc-heartbeat.md

@@ -107,7 +107,7 @@ few weeks, so stay tuned. Another big initative is adding videos to our docs:
 since video seems like a popular format for a lot of learners, we're working to
 supplement our official docs with embedded videos. Check out our first
 installment on the
-[Getting Started with Data Versioning](https://dvc.org/doc/start/data-versioning).
+[Getting Started with Data Versioning](/doc/start/data-and-model-versioning).
 
 https://youtu.be/kLKBcPonMYw
 

content/blog/2020-11-11-november-20-dvc-heartbeat.md

@@ -64,7 +64,7 @@ welcome referrals if you know a good candidate)!
 
 We're continuing to develop our video docs, and now half of our "Getting
 Started" section has video accompaniments. Check out our latest release on
-[data access with DVC](https://dvc.org/doc/start/data-access):
+[data access with DVC](/doc/start/data-and-model-access):
 
 https://youtu.be/EE7Gk84OZY8
 

content/blog/2020-12-18-december-20-dvc-heartbeat.md

@@ -53,17 +53,17 @@ As you may have heard
 on adding complete video docs to the "Getting Started" section of the DVC site.
 We now have 100% coverage! We have videos that mirror the tutorials for:
 
-- [Data versioning](https://dvc.org/doc/start/data-versioning) - how to use Git
-  and DVC together to track different versions of a dataset
+- [Data versioning](/doc/start/data-and-model-versioning) - how to use Git and
+  DVC together to track different versions of a dataset
 
-- [Data access](https://dvc.org/doc/start/data-access) - how to share models and
+- [Data access](/doc/start/data-and-model-access) - how to share models and
   datasets across projects and environments
 
-- [Pipelines](https://dvc.org/doc/start/data-pipelines) - how to create
-  reproducible pipelines to transform datasets to features to models
+- [Pipelines](/doc/start/data-pipelines) - how to create reproducible pipelines
+  to transform datasets to features to models
 
-- [Experiments](https://dvc.org/doc/start/experiments) - how to do a `git diff`
-  for models that compares and visualizes metrics
+- [Experiments](/doc/start/experiments) - how to do a `git diff` for models that
+  compares and visualizes metrics
 
 https://media.giphy.com/media/L4ZZNbDpOCfiX8uYSd/giphy.gif
 

content/docs/command-reference/diff.md

@@ -123,8 +123,9 @@ $ dvc diff
 
 Let's checkout the
 [2-track-data](https://github.com/iterative/example-get-started/releases/tag/2-track-data)
-tag, corresponding to the [Data Versioning](/doc/start/data-versioning) _Get
-Started_ chapter, right after we added `data.xml` file with DVC:
+tag, corresponding to the
+[Data Versioning](/doc/start/data-and-model-versioning) _Get Started_ chapter,
+right after we added `data.xml` file with DVC:
 
 ```dvc
 $ git checkout 2-track-data

content/docs/command-reference/get.md

@@ -151,7 +151,7 @@ file or directory from. It also has the `--out` option to specify the location
 to place the target data within the workspace. Combining these two options
 allows us to do something we can't achieve with the regular `git checkout` +
 `dvc checkout` process – see for example the
-[Get Older Data Version](/doc/start/data-versioning#switching-between-versions)
+[Get Older Data Version](/doc/start/data-and-model-versioning#switching-between-versions)
 chapter of our _Get Started_.
 
 Let's use the

content/docs/command-reference/import-url.md

@@ -190,8 +190,8 @@ $ git checkout 3-config-remote
 ## Example: Tracking a file from the web
 
 An advanced alternate to the intro of the
-[Versioning Basics](/doc/start/data-versioning) part of the _Get Started_ is to
-use `dvc import-url`:
+[Versioning Basics](/doc/start/data-and-model-versioning) part of the _Get
+Started_ is to use `dvc import-url`:
 
 ```dvc
 $ dvc import-url https://data.dvc.org/get-started/data.xml \

content/docs/command-reference/import.md

@@ -67,8 +67,8 @@ data `path`, and the `outs` field contains the corresponding local path in the
 <abbr>workspace</abbr>. It records enough metadata about the imported data to
 enable DVC efficiently determining whether the local copy is out of date.
 
-To actually [version the data](/doc/start/data-versioning), `git add` (and
-`git commit`) the import `.dvc` file.
+To actually [version the data](/doc/start/data-and-model-versioning), `git add`
+(and `git commit`) the import `.dvc` file.
 
 Note that `dvc repro` doesn't check or update import `.dvc` files (see
 `dvc freeze`), use `dvc update` to bring the import up to date from the data

content/docs/sidebar.json

@@ -35,13 +35,13 @@
     },
     "children": [
       {
-        "slug": "data-versioning",
+        "slug": "data-and-model-versioning",
         "tutorials": {
           "katacoda": "https://katacoda.com/dvc/courses/get-started/versioning"
         }
       },
       {
-        "slug": "data-access",
+        "slug": "data-and-model-access",
         "tutorials": {
           "katacoda": "https://katacoda.com/dvc/courses/get-started/accessing"
         }

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

@@ -1,13 +1,13 @@
 ---
-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, we've learned how to _track_ data and models with DVC, and how to commit
+their versions to Git. The next questions are: How can we _use_ these artifacts
+outside of the project? How do I download a model to deploy it? How to download
+a specific version of a model? Or 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.'
 ---
@@ -14,8 +14,8 @@ to a different version of a 100Gb file in less than a second with a
 `git checkout`.
 
 The foundation of DVC consists of a few commands that you can run along with
-`git` to track large files, directories, or ML models. Think "Git for data".
-Read on or watch our video to learn about versioning data with DVC!
+`git` to track large files, directories, or ML model files. Think "Git for
+data". Read on or watch our video to learn about versioning data with DVC!
 
 https://youtu.be/kLKBcPonMYw
 
@@ -34,8 +34,8 @@ $ dvc get https://github.com/iterative/dataset-registry \
 ```
 
 We use the fancy `dvc get` command to jump ahead a bit and show how Git repo
-becomes a source for datasets or models - what we call "data registry" or "model
-registry". `dvc get` can download any file or directory tracked in a <abbr>DVC
+becomes a source for datasets or models - what we call "data/model registry".
+`dvc get` can download any file or directory tracked in a <abbr>DVC
 repository</abbr>. It's like `wget`, but for DVC or Git repos. In this case we
 download the latest version of the `data.xml` file from the
 [dataset registry](https://github.com/iterative/dataset-registry) repo as the
@@ -90,10 +90,10 @@ outs:
 
 ## Storing and sharing
 
-You can upload DVC-tracked data or models with `dvc push`, so they're safely
-stored [remotely](/doc/command-reference/remote). This also means they can be
-retrieved on other environments later with `dvc pull`. First, we need to setup a
-storage:
+You can upload DVC-tracked data or model files with `dvc push`, so they're
+safely stored [remotely](/doc/command-reference/remote). This also means they
+can be retrieved on other environments later with `dvc pull`. First, we need to
+setup a storage:
 
 ```dvc
 $ dvc remote add -d storage s3://mybucket/dvcstore
@@ -154,9 +154,9 @@ a3
 
 ## Retrieving
 
-Having DVC-tracked data stored remotely, it can be downloaded when needed in
-other copies of this <abbr>project</abbr> with `dvc pull`. Usually, we run it
-after `git clone` and `git pull`.
+Having DVC-tracked data and models stored remotely, it can be downloaded when
+needed in other copies of this <abbr>project</abbr> with `dvc pull`. Usually, we
+run it after `git clone` and `git pull`.
 
 <details>
 

content/docs/start/data-pipelines.md

@@ -143,8 +143,8 @@ 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/start/data-versioning#storing-and-sharing), (usually along
-with `git commit` to version `dvc.yaml` itself).
+[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/experiments.md

@@ -172,8 +172,8 @@ $ git commit -a -m "Preserve best random forest experiment"
 ## Sharing experiments
 
 After committing the best experiments to our Git branch, we can
-[store and share](/doc/start/data-versioning#storing-and-sharing) them remotely
-like any other iteration of the pipeline.
+[store and share](/doc/start/data-and-model-versioning#storing-and-sharing) them
+remotely like any other iteration of the pipeline.
 
 ```dvc
 dvc push

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.

content/docs/use-cases/data-registries.md

@@ -2,10 +2,10 @@
 
 One of the main uses of <abbr>DVC repositories</abbr> is the
 [versioning of data and model files](/doc/use-cases/data-and-model-files-versioning).
-DVC also enables cross-project [reusability](/doc/start/data-access) of these
-<abbr>data artifacts</abbr>. This means that your projects can depend on data
-from other DVC repositories — like a **package management system for data
-science**.
+DVC also enables cross-project [reusability](/doc/start/data-and-model-access)
+of these <abbr>data artifacts</abbr>. This means that your projects can depend
+on data from other DVC repositories — like a **package management system for
+data science**.
 
 ![](/img/data-registry.png) _Data management middleware_
 

content/docs/use-cases/versioning-data-and-model-files/index.md

@@ -65,7 +65,7 @@ Benefits of our approach include:
 - **Collaboration**: Easily distribute your project development and share its
   data [internally](/doc/use-cases/shared-development-server) and
   [remotely](/doc/use-cases/sharing-data-and-model-files), or
-  [reuse](/doc/start/data-access) it in other places.
+  [reuse](/doc/start/data-and-model-access) it in other places.
 
 - **Data compliance**: Review data modification attempts as Git
   [pull requests](https://www.dummies.com/web-design-development/what-are-github-pull-requests/).

content/docs/user-guide/project-structure/dvc-files.md

@@ -3,7 +3,8 @@
 You can use `dvc add` to track data files or directories located in your current
 <abbr>workspace</abbr>\*. Additionally, `dvc import` and `dvc import-url` let
 you bring data from external locations to your project, and start tracking it
-locally. See [Data Versioning](/doc/start/data-versioning) for more info.
+locally. See [Data Versioning](/doc/start/data-and-model-versioning) for more
+info.
 
 > \* Certain [external locations](/doc/user-guide/managing-external-data) are
 > also supported.

redirects-list.json

@@ -23,6 +23,8 @@
   "^/(?:docs|documentation)(/.*)?$                                                        /doc$1",
 
   "^/doc/get-started(/.*)?$                                                               /doc/start",
+  "^/doc/start/data-versioning$                                                           /doc/start/data-and-model-versioning",
+  "^/doc/start/data-access$                                                               /doc/start/data-and-model-access",
   "^/doc/tutorial(/.*)?$                                                                  /doc/start",
   "^/doc/tutorials/get-started(/.*)?$                                                     /doc/start",
   "^/doc/tutorials/versioning(/.*)?$                                                      /doc/use-cases/versioning-data-and-model-files/tutorial",