Merge pull request #1857 from iterative/gs-data-pipelines-seo

start: add meta title, description and key term (data pipelines) [SEO]

content/docs/start/data-pipelines.md

@@ -1,13 +1,21 @@
-# Data Pipelines
+---
+title: 'Get Started: Data Pipelines'
+description: 'Learn how to build and use DVC pipelines to capture, organize,
+version, and reproduce your data science and machine learning workflows.'
+---
+
+# Get Started: Data Pipelines
 
 Versioning large data files and directories for data science is great, but not
 enough. How is data filtered, transformed, or used to train ML models? DVC
 introduces a mechanism to capture _data pipelines_ — series of data processes
 that produce a final result.
 
 DVC pipelines and their data can also be easily versioned (using Git). This
-allows you to better organize your project, and reproduce your workflow and
-results later exactly as they were built originally!
+allows you to better organize projects, and reproduce your workflow and results
+later — exactly as they were built originally! For example, you could capture a
+simple ETL workflow, organize a data science project, or build a detailed
+machine learning pipeline.
 
 ## Pipeline stages
 
@@ -299,7 +307,7 @@ important problems:
   and which commands will generate the pipeline results (such as an ML model).
   Storing these files in Git makes it easy to version and share.
 - _Continuous Delivery and Continuous Integration (CI/CD) for ML_ - describing
-  projects in way that it can be reproduced (built) is the fist necessary step
+  projects in way that it can be reproduced (built) is the first necessary step
   before introducing CI/CD systems. See our sister project,
   [CML](https://cml.dev/) for some examples.