guide: absorb What is DVC? into other existing docs, et al.

content/docs/how-to/best-practices.md

@@ -0,0 +1,41 @@
+# Best Practices
+
+Data scientists, engineers, or managers may already know or can easily find
+answers to some of these questions. However, the variety of answers and
+approaches makes data science collaboration a nightmare. **A systematic approach
+is required.**
+
+## Source code and data versioning
+
+- How do you avoid discrepancies between
+  [revisions](https://git-scm.com/docs/revisions) of source code and versions of
+  data files, when the data cannot fit into a traditional repository?
+
+## Experiment time log
+
+- How do you track which of your
+  [hyperparameter](<https://en.wikipedia.org/wiki/Hyperparameter_(machine_learning)>)
+  changes contributed the most to producing or improving your target
+  [metric](/doc/command-reference/metrics)? How do you monitor the degree of
+  each change?
+
+## Navigating through experiments
+
+- How do you recover a model from last week without wasting time waiting for the
+  model to retrain?
+
+- How do you quickly switch between a large dataset and a small subset without
+  modifying source code?
+
+## Reproducibility
+
+- How do you run a model's evaluation process again without retraining the model
+  and preprocessing a raw dataset?
+
+## Managing and sharing large data files
+
+- How do you share models trained in a GPU environment with colleagues who don't
+  have access to a GPU?
+
+- How do you share the entire 147 GB of your ML project, with all of its data
+  sources, intermediate data files, and models?

content/docs/sidebar.json

@@ -86,11 +86,7 @@
         "slug": "what-is-dvc",
         "label": "What is DVC?",
         "source": "what-is-dvc/index.md",
-        "children": [
-          "collaboration-issues",
-          "core-features",
-          "related-technologies"
-        ]
+        "children": ["core-features", "related-technologies"]
       },
       {
         "label": "DVC Files and Directories",
@@ -144,6 +140,16 @@
       }
     ]
   },
+  {
+    "slug": "how-to",
+    "source": false,
+    "children": [
+      {
+        "slug": "best-practices",
+        "source": "how-to/best-practices.md"
+      }
+    ]
+  },
   {
     "slug": "command-reference",
     "source": "command-reference/index.md",

content/docs/user-guide/index.md

@@ -2,11 +2,22 @@
 
 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
-manual. Their topics range from more technical foundations, impacting more parts
-of DVC, to more advanced and specific things you can do. We also include a few
-guides related to contributing to
+guides don't focus on specific scenarios, but have a general scope – like a
+_user manual_. Their topics range from more technical foundations, impacting
+more parts of DVC, to more advanced and specific things you can do. We also
+include a few guides related to contributing to
 [this open-source project](https://github.com/iterative/dvc).
 
+## What is DVC?
+
+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
+collaborating effectively. 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.
+
+---
+
 Please choose from the navigation sidebar to the left, or click the `Next`
 button below ↘