docs: add key terms to use case intros/tutorial and what is dvc? docs [SEO]

content/docs/use-cases/index.md

@@ -1,28 +1,28 @@
 # Use Cases
 
-We provide short articles on common ML workflow or data management scenarios
-that DVC can help with or improve. Our use cases are not written to be run
-end-to-end like tutorials. For more general, hands-on experience with DVC,
-please see our [Get Started](/doc/tutorials/get-started) instead.
+We provide short articles on common ML workflow and data science use cases that
+DVC can help with or improve. Our use cases are not written to be run end-to-end
+like tutorials. For more general, hands-on experience with DVC, please see our
+[Get Started](/doc/tutorials/get-started) instead.
 
 ## Why DVC?
 
 Even with all the success we've seen today in machine learning (ML), especially
-with deep learning and its applications in business, the data science community
-still lacks good practices for organizing their projects and collaborating
-effectively. This is a critical challenge: while ML algorithms and methods are
-no longer tribal knowledge, they are still difficult to implement, reuse, and
-manage.
+with deep learning and its applications in business, data scientists still lack
+good tools for organizing their projects and collaborating effectively. This is
+a critical challenge: while ML algorithms and methods are no longer tribal
+knowledge, they are still difficult to implement, reuse, and manage.
 
 ## Basic uses of DVC
 
 If you store and process data files or datasets to produce other data or machine
 learning models, and you want to
 
 - capture and save <abbr>data artifacts</abbr> the same way you capture code;
-- track and switch between different versions of data or models easily;
-- understand how data or models were built in the first place;
-- be able to compare models and metrics to each other;
+- track, control, and switch between different versions of data or models
+  easily;
+- understand how machine learning models were built in the first place;
+- compare ML models and metrics to each other;
 - bring software engineering best practices to your data science team
 
 DVC is for you!

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

@@ -11,8 +11,8 @@ pull requests, etc.)
 
 To actually store the data, DVC uses a built-in <abbr>cache</abbr>, and supports
 synchronizing it with various types of
-[remote storage](/doc/command-reference/remote). This allows storing and sharing
-data easily, and alongside code.
+[remote storage](/doc/command-reference/remote). This allows for easy data and
+model versioning, storage, and sharing — right alongside code.
 
 ![](/img/model-versioning-diagram.png) _Code and data flows in DVC_
 
@@ -30,9 +30,9 @@ on-premises storage (e.g. SSH, NAS) as well as any major cloud storage provider
 ## DVC is not Git!
 
 DVC metafiles such as `dvc.yaml` and `.dvc` files serve as placeholders to track
-data files and directories (among other purposes). They point to specific data
-contents in the <abbr>cache</abbr>, providing the ability to store multiple data
-versions out-of-the-box.
+the version of data files and directories (among other purposes). They point to
+specific data contents in the <abbr>cache</abbr>, providing the ability to store
+multiple data versions out-of-the-box.
 
 Full-fledged
 [version control](https://git-scm.com/book/en/v2/Getting-Started-About-Version-Control)
@@ -46,7 +46,7 @@ several other novel features (see [Get Started](/doc/start/) for a primer.)
 
 Let's say you have an empty <abbr>DVC repository</abbr> and put a dataset of
 images in the `images/` directory. You can start tracking it with `dvc add`.
-This generate a `.dvc` file, which can be committed to Git in order to save the
+This generates a `.dvc` file, which can be committed to Git in order to save the
 project's version:
 
 ```dvc
@@ -116,7 +116,8 @@ M       model.pkl
 ```
 
 However, we can checkout certain parts only, for example if we want to keep the
-latest source code and model but rewind to the previous dataset only:
+latest source code and model versions, but rewind to the previous version of the
+dataset:
 
 ```dvc
 $ git checkout v1.0 images.dvc
@@ -125,5 +126,5 @@ M       images
 ```
 
 DVC [optimizes](/doc/user-guide/large-dataset-optimization) this operation by
-avoiding copying files each time, so checking out data is quick even if you have
-large data files.
+avoiding copying files each time, so checking out data is quick even if you are
+versioning large data files.

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

@@ -1,8 +1,8 @@
-# Tutorial: Versioning
+# Tutorial: Data & Model Versioning
 
 The goal of this example is to give you some hands-on experience with a basic
-machine learning version control scenario: working with multiple versions of
-datasets and ML models using DVC commands. We'll work with a
+machine learning version control scenario: managing multiple dataset and ML
+model versions using DVC commands. We'll work with a
 [tutorial](https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html)
 that [François Chollet](https://twitter.com/fchollet) put together to show how
 to build a powerful image classifier using a pretty small dataset.
@@ -237,9 +237,9 @@ $ git commit -m "Second model, trained with 2000 images"
 $ git tag -a "v2.0" -m "model v2.0, 2000 images"
 ```
 
-That's it! We have tracked a second dataset, model, and metrics versioned DVC,
-and the DVC-files that point to them committed with Git. Let's now look at how
-DVC can help us go back to the previous version if we need to.
+That's it! We've tracked the second version of the dataset, model, and metrics
+in DVC and committed the DVC-files that point to them with Git. Now let's look
+at how DVC can help us go back to the previous version if we need to.
 
 ## Switching between workspace versions
 
@@ -338,15 +338,15 @@ changed. For example, when we added new images to built the second version of
 our model, that was a dependency change. It also updates outputs and puts them
 into the <abbr>cache</abbr>.
 
-To make things a little simpler: if `dvc add` and `dvc checkout` provide a basic
-mechanism to version control large data files or models, `dvc run` and
-`dvc repro` provide a build system for ML models, which is similar to
+To make things a little simpler: `dvc add` and `dvc checkout` provide a basic
+mechanism for model and large dataset versioning. `dvc run` and `dvc repro`
+provide a build system for machine learning models, which is similar to
 [Make](https://www.gnu.org/software/make/) in software build automation.
 
 ## What's next?
 
-In this example, our focus was on giving you hands-on experience with versioning
-ML models and datasets. We specifically looked at the `dvc add` and
+In this example, our focus was on giving you hands-on experience with dataset
+and ML model versioning. We specifically looked at the `dvc add` and
 `dvc checkout` commands. We'd also like to outline some topics and ideas you
 might be interested to try next to learn more about DVC and how it makes
 managing ML projects simpler.

content/docs/user-guide/what-is-dvc.md

@@ -1,16 +1,17 @@
 # What Is DVC?
 
-**Data Version Control** is a new type of data versioning, workflow and
+**Data Version Control** is a new type of data versioning, workflow, and
 experiment management software, that builds upon [Git](https://git-scm.com/)
 (although it can work stand-alone). DVC reduces the gap between established
 engineering tool sets and data science needs, allowing users to take advantage
 of new [features](#core-features) while reusing existing skills and intuition.
 
 ![](/img/reproducibility.png) _DVC codifies data and ML experiments_
 
-Data science experiment sharing and collaboration can be done through a regular
-Git flow (commits, branching, pull requests, etc.), the same way it works for
-software engineers.
+The same way that software engineers use Git, data scientists can use DVC to
+apply a regular flow to project sharing and collaboration (commits, branching,
+pull requests, etc.). Using Git and DVC, data science and machine learning teams
+can version experiments, manage large datasets, and make projects reproducible.
 
 ## Core Features
 
@@ -22,7 +23,7 @@ software engineers.
   [versioning](/doc/use-cases/versioning-data-and-model-files) capabilities.
 
 - **Data versioning** is enabled by replacing large files, dataset directories,
-  ML models, etc. with small
+  machine learning models, etc. with small
   [metafiles](/doc/user-guide/dvc-files-and-directories) (easy to handle with
   Git). These placeholders point to the original data, which is decoupled from
   source code management.