start: reader instructions (in index pages)

content/docs/sidebar.json

@@ -53,7 +53,7 @@
       },
       {
         "slug": "experiment-management",
-        "source": false,
+        "source": "experiment-management/index.md",
         "children": ["experiments", "visualization"]
       }
     ]

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

@@ -15,3 +15,45 @@ We can also build and version pipelines to capture our data workflows stage by
 stage, from raw data and its pre-processing, through feature engineering and ML
 model training, and up to evaluation (performance metrics), visualization, or
 other post-processing.
+
+## Following the Get Started
+
+Each page in this trail is an independent chapter you can go to directly,
+although they are presented in a good order if you plan to cover them all. There
+may be linked notes and references about other chapters or docs, but you can
+skip them and still get a good idea of the features in question, even by only
+reading page sections.
+
+For better learning, you can also try each step yourself and get to similar
+results. Some of the required steps for this may be inside collapsed sections
+you can click on to expand:
+
+<details>
+
+### Click for an example!
+
+This is just an example of a collapsed details section. Click again to close it
+back.
+
+</details>
+
+<cards>
+
+  <card href="/doc/start/data-management/data-versioning" heading="Data Versioning">
+    Base layer of DVC for large files, datasets, and machine learning models
+  </card>
+
+  <card href="/doc/start/data-management/data-and-model-access" heading="Data and Model Access">
+    Using data artifacts from outside of the project and importing them from
+    another DVC project
+  </card>
+
+  <card href="/doc/start/data-management/data-pipelines" heading="Data Pipelines">
+    Describe how models and other data artifacts are built.
+  </card>
+
+  <card href="/doc/start/data-management/metrics-parameters-plots" heading="Metrics, Parameters, and Plots">
+    Capture, navigate, and evaluate ML projects.
+  </card>
+
+</cards>

content/docs/start/experiment-management/index.md

@@ -0,0 +1,47 @@
+---
+title: 'Get Started: Experiment Management'
+description:
+  'Get started with experiment management and visualization in DVC, without
+  bloating Git history.'
+---
+
+# Get Started: Experiment Management
+
+Let's look at DVC's features from the perspective of machine learning experiment
+management. This includes bootstrapping, running and queueing experiments
+(possibly in parallel), as well as visually comparing the results to commit the
+best ones to Git.
+
+## Following the Get Started
+
+Each page in this trail is an independent chapter you can go to directly,
+although they are presented in a good order if you plan to cover them all. There
+may be linked notes and references about other chapters or docs, but you can
+skip them and still get a good idea of the features in question, even by only
+reading page sections.
+
+For better learning, you can also try each step yourself and get to similar
+results. Some of the required steps for this may be inside collapsed sections
+you can click on to expand:
+
+<details>
+
+### Click for an example!
+
+This is just an example of a collapsed details section. Click again to close it
+back.
+
+</details>
+
+<cards>
+
+  <card href="/doc/start/experiment-management/experiments" heading="Experiments">
+    Enable exploration, iteration, and comparison across many trials in ML
+    projects.
+  </card>
+
+  <card href="/doc/start/experiment-management/visualization" heading="Visualization">
+    Compare experiment results visually.
+  </card>
+
+</cards>

content/docs/start/index.md

@@ -45,20 +45,22 @@ $ git commit -m "Initialize DVC"
 
 Now you're ready to DVC!
 
-DVC's multiple feature sets are best understood from different angles. Pick a
-trail below to see an overview of all features from that perspective:
+The value of DVC's many feature sets is best understood from different angles.
+Pick a trail below to see an overview of all features from that perspective:
 
 ### Data Management
 
+- [Introduction](/doc/start/data-management)
+
 - **[Data and model versioning]** is the base layer of DVC for large files,
   datasets, and machine learning models. Use a regular Git workflow, but without
   storing large files in the repo (think "Git for data"). Data is stored
   separately, which allows for efficient sharing.
 
-- **[Data and model access]** shows how to use data artifacts from outside of
-  the project and how to import data artifacts from another DVC project. This
-  can help to download a specific version of an ML model to a deployment server
-  or import a model to another project.
+- **[Data and model access]** goes over using data artifacts from outside of the
+  project and importing them from another DVC project. This can help to download
+  a specific version of an ML model to a deployment server or import a model to
+  another project.
 
 - **[Data pipelines]** describe how models and other data artifacts are built,
   and provide an efficient way to reproduce them. Think "Makefiles for data and
@@ -76,10 +78,12 @@ trail below to see an overview of all features from that perspective:
 
 ### Experimentation
 
-- **[Experiments]** enable exploration, iteration, and comparison across many ML
-  experiments. Track your experiments with automatic versioning and checkpoint
-  logging. Compare differences in parameters, metrics, code, and data. Apply,
-  drop, roll back, resume, or share any experiment.
+- [Introduction](/doc/start/experiment-management)
+
+- **[Experiments]** enable exploration, iteration, and comparison across many
+  trials in ML projects. Track your experiments with automatic versioning and
+  checkpoint logging. Compare differences in parameters, metrics, code, and
+  data. Apply, drop, roll back, resume, or share any experiment.
 
 - **[Visualization]** compare experiment results visually, track your plots and
   generate them with library integrations.

redirects-list.json

@@ -45,7 +45,7 @@
   "^/doc/start/data-and-model-access(/.*)?$                                               /doc/start/data-management/data-and-model-access 302",
   "^/doc/start/data-pipelines(/.*)?$                                                      /doc/start/data-management/data-pipelines 302",
   "^/doc/start/metrics-parameters-plots(/.*)?$                                            /doc/start/data-management/metrics-parameters-plots 302",
-  "^/doc/start/experiments$                                                               /doc/start/experiment-management/experiments 302",
+  "^/doc/start/experiments$                                                               /doc/start/experiment-management 302",
   "^/doc/start/experiments/visualization$                                                 /doc/start/experiment-management/visualization 302",
   "^/doc/tutorial(/.*)?$                                                                  /doc/start",
   "^/doc/tutorials(/.*)?                                                                  /doc/start",