Plots: mention the VS Code Extension for visualization

content/docs/command-reference/plots/index.md

@@ -44,13 +44,18 @@ DVC can work with two types of plots files:
 1. Data series files, which can be JSON, YAML, CSV or TSV.
 2. Image files in JPEG, GIF, or PNG format.
 
-DVC generates plots as static HTML webpages that can be open with a web browser.
-They can also be saved as SVG or PNG image files from the browser.
+DVC generates plots as static HTML webpages you can open with a web browser
+(they can be saved as SVG or PNG image files from there). You can also visualize
+DVC plots from the VS Code Extension, which includes a special [Plots Dashboard]
+that corresponds to the features in the `dvc plots` commands.
 
 Data-series plots utilize [Vega-Lite](https://vega.github.io/vega-lite/) for
 rendering (declarative JSON grammar for defining graphics). Image-type plots are
 rendered using `<img>` tags directly.
 
+[plots dashboard]:
+  https://github.com/iterative/vscode-dvc/blob/main/extension/resources/walkthrough/plots.md
+
 ## Supported file formats
 
 Image-type plots are included in HTML as-is, without additional processing.

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

@@ -132,7 +132,10 @@ Modifying stage 'evaluate' in 'dvc.yaml'
 
 ```
 
-Now let's view the plots:
+Now let's view the plots. You can run `dvc plots show` on your Terminal (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 [Plots Dashboard] of the [DVC Extension] to
+visualize them.
 
 ```dvc
 $ dvc plots show
@@ -156,6 +159,11 @@ Later we will see how to
 For now, let's see how can we capture another important piece of information
 which will be useful for comparison: parameters.
 
+[plots dashboard]:
+  https://github.com/iterative/vscode-dvc/blob/main/extension/resources/walkthrough/plots.md
+[dvc extension]:
+  https://marketplace.visualstudio.com/items?itemName=Iterative.dvc
+
 ## Defining stage parameters
 
 It's pretty common for data science pipelines to include configuration files

content/docs/start/experiments/visualization.md

@@ -27,12 +27,11 @@ actual,predicted
 > We added a [loop] comparing the results to generate this file from the
 > predictions.
 
-[loop]:
-  https://github.com/iterative/example-dvc-experiments/blob/main/src/train.py#L123
-[confusion matrix]: https://en.wikipedia.org/wiki/Confusion_matrix
-
 Running the experiment with `dvc exp run` will produce `plots/confusion.csv`.
-Use `dvc plots show` to present it as an HTML file, and open it in the browser:
+You can run `dvc plots show` on your Terminal to generate and embed them in an
+HTML file you can open in the browser (shown below). Or you can load your
+project in VS Code and use the [Plots Dashboard] of the [DVC Extension] to
+visualize them.
 
 ```dvc
 $ dvc plots show plots/confusion.csv --template confusion \
@@ -42,6 +41,14 @@ file:///.../example-dvc-experiments/plots/confusion.json.html
 
 ![confusion matrix](/img/start_visualization_confusion1.png)
 
+[confusion matrix]: https://en.wikipedia.org/wiki/Confusion_matrix
+[loop]:
+  https://github.com/iterative/example-dvc-experiments/blob/main/src/train.py#L123
+[plots dashboard]:
+  https://github.com/iterative/vscode-dvc/blob/main/extension/resources/walkthrough/plots.md
+[dvc extension]:
+  https://marketplace.visualstudio.com/items?itemName=Iterative.dvc
+
 ## Displaying user-generated plot images
 
 Let's produce another plot to see misclassified examples from each class. This
@@ -55,7 +62,7 @@ project.][misclassified-example-code]
   https://github.com/iterative/example-dvc-experiments/blob/48b1e5078c957f71674c00f416290eaa3b20b559/src/util.py#L49
 
 ```dvc
-$ dvc plots show plots/misclassified.png
+$ dvc plots show --open plots/misclassified.png
 ```
 
 ![Misclassification table](/img/start_visualization_misclassification.png)