Get started - "top funnel" changes

content/docs/sidebar.json

@@ -44,7 +44,6 @@
         "source": false,
         "children": [
           "data-versioning",
-          "data-and-model-access",
           "data-pipelines",
           {
             "label": "Metrics, Parameters, and Plots",
@@ -53,13 +52,19 @@
         ]
       },
       {
+        "label": "Experiment Management",
         "slug": "experiments",
         "source": false,
         "children": [
-          "experiment-versioning",
-          "experiment-management",
-          "building-pipelines",
-          "experiment-iterations"
+          "experiment-tracking",
+          {
+            "label": "Collaborating on Experiments",
+            "slug": "experiment-collaboration"
+          },
+          {
+            "label": "Experimenting Using Pipelines",
+            "slug": "experiment-pipelines"
+          }
         ]
       }
     ]
@@ -129,7 +134,6 @@
         "slug": "data-management",
         "source": false,
         "children": [
-          "large-dataset-optimization",
           {
             "slug": "remote-storage",
             "source": "remote-storage/index.md",
@@ -161,8 +165,10 @@
             ]
           },
           "cloud-versioning",
+          "discovering-and-accessing-data",
           "importing-external-data",
-          "managing-external-data"
+          "managing-external-data",
+          "large-dataset-optimization"
         ]
       },
       {

content/docs/start/data-management/data-pipelines.md

@@ -25,6 +25,10 @@ later — exactly as they were built originally! For example, you could capture
 simple ETL workflow, organize a data science project, or build a detailed
 machine learning pipeline.
 
+Later on, we will find DVC manages the execution of
+[machine learning experiments](/doc/start/experiments/experiment-pipelines) on
+top of these pipelines - controlling their execution, injecting parameters, etc.
+
 ## Pipeline stages
 
 Use `dvc stage add` to create _stages_. These represent processes (source code

content/docs/start/data-management/data-versioning.md

@@ -287,6 +287,15 @@ layer. DVC in turn manipulates `.dvc` files, whose contents define the data file
 versions. DVC also synchronizes DVC-tracked data in the <abbr>workspace</abbr>
 efficiently to match them.
 
+## Discovering and accessing data
+
+DVC helps you with accessing and using your data artifacts from outside of the
+project where they are versioned, and your tracked data can be imported and
+fetched from anywhere. For example, you may want to download a specific version
+of an ML model to a deployment server or import a dataset into another project.
+To learn about how DVC allows you to do this, see the
+[discovering and accessing data guide](/doc/user-guide/data-management/discovering-and-accessing-data).
+
 ## Large datasets versioning
 
 In cases where you process very large datasets, you need an efficient mechanism

content/docs/start/data-management/metrics-parameters-plots.md

@@ -50,7 +50,7 @@ $ dvc repro
 
 The `-O` option here specifies an output that will not be <abbr>cached</abbr> by
 DVC, and `-M` specifies a metrics file (that will also not be cached).
-`dvc stage add` generates a new stage in the `dvc.yaml` file:
+`dvc stage add` will generates this new stage in the `dvc.yaml` file:
 
 ```yaml
 evaluate:
@@ -84,7 +84,7 @@ files to be versioned by Git.
 </details>
 
 [`evaluate.py`] writes the model's [ROC-AUC] and [average precision] to
-`eval/live/metrics.json` (previously marked as a [metrics file] with `-M`):
+`eval/live/metrics.json` (designated a [metrics file] with `-M` above):
 
 ```json
 {
@@ -160,7 +160,7 @@ plots:
   - eval/importance.png
 ```
 
-To generate them, you can run `dvc plots show` (shown below), which generates an
+To render them, you can run `dvc plots show` (shown below), which generates an
 HTML file you can open in a browser. Or you can load your project in VS Code and
 use the [DVC Extension]'s [Plots Dashboard].
 

content/docs/start/experiments/experiment-management.md → content/docs/start/experiments/experiment-collaboration.md

@@ -1,16 +1,20 @@
 ---
-title: 'Get Started: Experiment Management'
+title: 'Get Started: Experiment Collaboration'
 description:
-  'Manage your experiments and share them with others using software engineering
-  best practices.'
+  'Share your experiments with others, persist and apply your changes using Git
+  branches and software engineering best practices.'
 ---
 
-# Get Started: Experiment Management
+# Get Started: Experiment Collaboration
 
-After having compared all the experiments, you still need to agree on which one
-is the best and manage the remaining candidates. <abbr>DVC Experiments</abbr>
-are fully compatible with Git workflows, so you can manage the experiments using
-software engineering best practices.
+After having compared some experiments' results and parameters, you still need
+to agree on which one is the best, share it, track it, and do some house keeping
+on the rejected experiments.
+
+<abbr>DVC Experiments</abbr> are fully compatible with Git workflows. You can
+share, manage and collaborate on experiments and related code changes using
+software engineering best practices. There is no need for a different system or
+paradigm to track and version experiments.
 
 ## Sharing
 
@@ -19,7 +23,7 @@ Unless you have enabled
 the <abbr>DVC experiments</abbr> only exist in your repo and people can't manage
 or view them from other machines.
 
-You can share an experiment with others from your machine:
+To share an experiment with others from your machine:
 
 <toggle>