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start: add meta title, description and key term (data pipelines) [SEO] #1857

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Oct 29, 2020
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start: add meta title, description and key term (data pipelines) [SEO] #1857

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a41f503
Adds p2 for 'machine learning pipelines' and custom meta description.
jeremydesroches Oct 12, 2020
aa5e30a
Reflow frontmatter description to 80 characters.
jeremydesroches Oct 13, 2020
4486d65
Update meta title, description, and H1.
jeremydesroches Oct 18, 2020
6ca7f62
Fix typo in CML bullet.
jeremydesroches Oct 18, 2020
de0cbf5
Update content/docs/start/data-pipelines.md
jorgeorpinel Oct 19, 2020
4cd2519
Update content/docs/start/data-pipelines.md
jeremydesroches Oct 22, 2020
2529780
Fix formatting in intro.
jeremydesroches Oct 22, 2020
4213217
Edit meta desc. Fix intro p2 phrasing.
jeremydesroches Oct 28, 2020
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14 changes: 11 additions & 3 deletions content/docs/start/data-pipelines.md
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@@ -1,4 +1,10 @@
# Data Pipelines
---
title: 'Get Started: Data Pipelines'
description: 'Get started with pipelines in DVC. Learn how to capture, organize,
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version, and reproduce your data science and machine learning workflows.'
---

# Get Started: Data Pipelines
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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
Expand All @@ -7,7 +13,9 @@ 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!
results later exactly as they were built originally! This allows you to capture
simple ETL workflows, better organize data science projects, or build detailed
machine learning pipelines (to name a few uses).
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## Pipeline stages

Expand Down Expand Up @@ -300,7 +308,7 @@ 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.

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