cases: copy edits to latest Vrng iteration, and append next steps par…

…agaph (bottom)

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

@@ -8,58 +8,58 @@ these files and directories?
 ![](/img/data-ver-complex.png) _Exponential complexity of data science projects_
 
 Another problem in the field has to do with bookkeeping: being able to identify
-past research inputs and processes to understand results, for knowledge sharing,
-or for debugging. There's also the matter of defining and enforcing data
-lifecycles.
+past research inputs and processes to understand results, for knowledge sharing
+or for debugging.
 
 **Data Version Control** (DVC) helps you organize data and models effectively,
 and capture their versions with
 [Git commits](https://git-scm.com/book/en/v2/Git-Basics-Recording-Changes-to-the-Repository).
 It supports data _versioning through codification_: describing which datasets,
 ML artifacts, etc. should be in the <abbr>workspace</abbr> at any given time.
 DVC also provides mechanisms to cache different data contents, and to switch
-between their versions.
+between these versions.
 
 ![](/img/project-versions.png) _DVC matches the right versions of data to the
 rest of your project._
 
+DVC makes projects fully _reproducible_:
+[Restore](/doc/command-reference/checkout) any project version and find the
+corresponding data instantly. At the same time, the data stays with you — free
+from Git hosting storage
+[constraints](https://docs.github.com/en/free-pro-team@latest/github/managing-large-files/what-is-my-disk-quota).
+
 This is achieved with special
 [metafiles](/doc/user-guide/dvc-files-and-directories) that can be put in Git
-instead of the actual data. And by storing unique data versions (no file
-duplication) separate from the workspace.
+instead of the actual data. And by storing unique data versions (preventing file
+duplication) separate from the workspace. Now file names don't need to change,
+because they can variable data. The result is a single immutable history for
+data, code, models, etc. — a proper experiments journal.
 
-The result is a single immutable history for data, code, models, etc., like an
-experiments journal. This makes the project fully _reproducible_:
-[Restore](/doc/command-reference/checkout) any project version and find the
-corresponding data instantly. And at the same time, the data stays with you —
-free from the storage constraints of Git repos and
-[hosting](https://docs.github.com/en/free-pro-team@latest/github/managing-large-files/what-is-my-disk-quota).
-
-> To learn how it looks and feels, try the
+> To learn how DVC looks and feels, try the
 > [versioning tutorial](/doc/use-cases/versioning-data-and-model-files/tutorial)
-> 👩‍💻. And there are many other [guides](/doc/user-guide) and
-> [references](/doc/command-reference) that explain DVC in more detail.
+> 👩‍💻. <br/> There are also other [guides](/doc/user-guide) and
+> [references](/doc/command-reference) that explain DVC in detail.
 
-These are the benefits of our approach:
+Benefits of our approach include:
 
 - **Lightweight**: DVC is a
   [free](https://github.com/iterative/dvc/blob/master/LICENSE), open-source
-  [command line](/doc/command-reference) tool that doesn't require databases or
-  servers.
+  [command line](/doc/command-reference) tool that doesn't require databases,
+  servers, or any other special services.
 
-- **Consistency**: Keep your projects readable with stable file names (which can
-  contain variable data). No need for complicated paths like
-  `data/20190922/labels_v7_final` or for constantly editing them in source code.
+- **Consistency**: Keep your projects readable with stable file names. No need
+  for complicated paths like `data/20190922/labels_v7_final` or for constantly
+  editing these in source code.
 
 - **Efficient data management**: Use a cost-effective on-premises or cloud
   storage of your choice for your data and ML models. DVC
-  [optimizes](/doc/user-guide/large-dataset-optimization) their storage and
+  [optimizes](/doc/user-guide/large-dataset-optimization) data storage and
   transfers.
 
-- **Collaboration**: Easily share project data
-  [internally](/doc/use-cases/shared-development-server) or
+- **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 elsewhere.
+  [reuse](/doc/start/data-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/).
@@ -72,5 +72,10 @@ These are the benefits of our approach:
   [data registries](/doc/use-cases/data-registries), and other best practices.
 
 In summary, data science and machine learning are iterative processes where the
-lifecycles of data, models, and code happen at different paces. DVC helps
-integrate and manage them effectively. There are other things that DVC can do
+lifecycles of data, models, and code happen at different paces. DVC helps you
+define, manage, and enforce them.
+
+And this is only the beginning. DVC comes with other [features](/features)
+specific to data science out-of-the-box. Build, run, and versioning
+[data pipelines](/doc/command-reference/dag), manage
+[experiments](/doc/start/experiments) effectively, and more!