iterative · VANRao-Stack · May 19, 2020 · May 19, 2020 · jorgeorpinel · May 19, 2020
diff --git a/content/docs/understanding-dvc/collaboration-issues.md b/content/docs/understanding-dvc/collaboration-issues.md
diff --git a/content/docs/understanding-dvc/core-features.md b/content/docs/understanding-dvc/core-features.md
diff --git a/content/docs/understanding-dvc/existing-tools.md b/content/docs/understanding-dvc/existing-tools.md
diff --git a/content/docs/understanding-dvc/related-technologies.md b/content/docs/understanding-dvc/related-technologies.md
diff --git a/content/docs/understanding-dvc/what-is-dvc.md b/content/docs/understanding-dvc/what-is-dvc.md
diff --git a/content/docs/use-cases/index.md b/content/docs/use-cases/index.md
@@ -1,5 +1,14 @@
 # Use Cases
 
+# Collaboration Issues in Data Science
+
+Even with all the success we've seen today in machine learning (ML),
+specifically deep learning and its applications in business, the data science
+community still lacks good practices for organizing their projects and
+effectively collaborating across their varied ML projects. This is a critical
+challenge: we need to evolve towards ML algorithms and methods no longer being
+tribal knowledge and making them easy to implement, reuse, and manage.
+
 We provide short articles on common ML workflow or data management scenarios
 that DVC can help with or improve. These include the motivating context (usually
 extracted from real-life cases); And the approaches to solving them can combine

diff --git a/...nt/docs/understanding-dvc/how-it-works.md → content/docs/user-guide/how-it-works.md b/...nt/docs/understanding-dvc/how-it-works.md → content/docs/user-guide/how-it-works.md
diff --git a/content/docs/user-guide/index.md b/content/docs/user-guide/index.md
@@ -1,5 +1,21 @@
 # User Guide
 
+Data Version Control, or DVC, is **a new type of experiment management
+software** that has been built **on top of the existing engineering toolset that
+you're already used to**, and particularly on a source code version control
+system (currently Git). DVC reduces the gap between existing tools and data
+science needs, allowing users to take advantage of experiment management
+software while reusing existing skills and intuition.
+
+The underlying source code control system eliminates the need to use external
+services. Data science experiment sharing and collaboration can be done through
+regular Git tools (commit messages, merges, pull requests, etc) the same way it
+works for software engineers.
+
+DVC implements a **Git experimentation methodology** where each experiment
+exists with its code as well as data, and can be represented as a separate Git
+branch or commit.
+
 Our guides describe the main DVC concepts and features comprehensively,
 explaining when and how to use them, as well as connections between them. These
 guides don't focus on specific scenarios, but have a general scope – like a user

diff --git a/content/docs/understanding-dvc/resources.md → content/docs/user-guide/resources.md b/content/docs/understanding-dvc/resources.md → content/docs/user-guide/resources.md