docs: add "machine learning pipeline" references, expand context in cmd ref, user guide [SEO]

content/docs/command-reference/dag.md

@@ -20,9 +20,9 @@ A data pipeline, in general, is a series of data processing
 input and produce an <abbr>output</abbr>). A pipeline may produce intermediate
 data, and has a final result.
 
-Data processing or ML pipelines typically start with large raw datasets, include
-intermediate featurization and training stages, and produce a final model, as
-well as accuracy [metrics](/doc/command-reference/metrics).
+Data science and machine learning pipelines typically start with large raw
+datasets, include intermediate featurization and training stages, and produce a
+final model, as well as accuracy [metrics](/doc/command-reference/metrics).
 
 In DVC, pipeline stages and commands, their data I/O, interdependencies, and
 results (intermediate or final) are specified in `dvc.yaml`, which can be
@@ -78,9 +78,10 @@ example in Bash, we could add the following line to `~/.bashrc`:
 export DVC_PAGER=more
 ```
 
-## Examples
+## Example: Visualize a DVC Pipeline
 
-Visualize DVC pipeline:
+Visualize the prepare, featurize, train, and evaluate stages of a pipeline as
+defined in `dvc.yaml`:
 
 ```dvc
 $ dvc dag

content/docs/command-reference/repro.md

@@ -30,6 +30,9 @@ results.
 > (either manually or by using `dvc run`) while initial data dependencies can be
 > registered with `dvc add`.
 
+To get hands-on experience with data science and machine learning pipelines, see
+[Get Started: Data Pipelines](/doc/start/data-pipelines).
+
 This command is similar to [Make](https://www.gnu.org/software/make/) in
 software build automation, but DVC captures build requirements
 ([dependencies and outputs](/doc/command-reference/run#dependencies-and-outputs))
@@ -175,9 +178,7 @@ up-to-date and only execute the final stage.
 
 ## Examples
 
-For simplicity, let's build a pipeline defined below. (If you want get your
-hands-on something more real, see this short
-[pipeline tutorial](/doc/start/data-pipelines)). It takes this `text.txt` file:
+Let's build and reproduce a simple pipeline. It takes this `text.txt` file:
 
 ```
 dvc

content/docs/command-reference/run.md

@@ -23,8 +23,11 @@ positional arguments:
 
 `dvc run` is a helper for creating or updating
 [pipeline](/doc/command-reference/dag) stages in a `dvc.yaml` file (located in
-the current working directory). _Stages_ represent individual data processes,
-including their input and resulting outputs.
+the current working directory).
+
+_Stages_ represent individual data processes, including their input and
+resulting outputs. They can be combined to capture simple data workflows,
+organize data science projects, or build detailed machine learning pipelines.
 
 A stage name is required and can be provided using the `-n` (`--name`) option.
 The other available [options](#options) are mostly meant to describe different

content/docs/user-guide/dvc-files-and-directories.md

@@ -100,8 +100,8 @@ and `dvc commit` commands, but not when a `.dvc` file is overwritten by
 
 ## `dvc.yaml` file
 
-`dvc.yaml` files describe data pipelines, similar to how
-[Makefiles](https://www.gnu.org/software/make/manual/make.html#Introduction)
+`dvc.yaml` files describe data science or machine learning pipelines, similar to
+how [Makefiles](https://www.gnu.org/software/make/manual/make.html#Introduction)
 work for building software. Its YAML structure contains a list of stages which
 can be written manually or generated by user code.