Repository: kedro-org/kedro
Pull request: Revise visualisation documentation for Plotly charts, Matplotlib
This PR is now ready for review.

## Description
Resolves kedro-org/kedro#1949 

I've made some improvements to the docs for Kedro-Viz in the "Visualisation with Kedro-Viz" section:

* Readability changes in the pages that introduce kedro viz and plotly reporting
* Improvements to use the spaceflights starter (a recent change pulled it out of the main spaceflights tutorial, this was a tidy-up to explain how to use plotly reporting by adding it on top of the starter code)
* Minor changes in experiment tracking, since that will be covered more comprehensively by a separate issue
* New screen shots for plotly charts

You can see the files changed here: https://stichbury.github.io/

It's probably easier to review the built HTML instead of the diffs but any feedback would be appreciated. Since we released 18.4 i've tried to rebuild the example and now I cannot get the chart to show in viz. I've also updated my Mac OS to the latest version and I've no idea which of those changes (or something else) has broken the charts.

Tested on Chrome & Safari.

## Checklist

- [ ] Read the [contributing](https://github.com/kedro-org/kedro/blob/main/CONTRIBUTING.md) guidelines
- [ ] Opened this PR as a 'Draft Pull Request' if it is work-in-progress
- [ ] Updated the documentation to reflect the code changes
- [ ] Added a description of this change in the [`RELEASE.md`](https://github.com/kedro-org/kedro/blob/main/RELEASE.md) file
- [ ] Added tests to cover my changes


<a href="https://gitpod.io/#https://github.com/kedro-org/kedro/pull/2061"><img src="https://gitpod.io/button/open-in-gitpod.svg"/></a>



Problem statement:
Improve visualisation sections for the basics and for Plotly reporting
## Description

The Kedro-Viz section of the docs needs to be updated. There are currently 3 pages and this issue is to address the first 2 pages "Get started" and "Visualise charts" in detail to make improvements for readability, clarity and correctness.

The final section on experiment tracking should have a cosmetic set of fixes to remove mention of earlier sections of the tutorial (since this is no longer part of that spaceflights tutorial) but I'll leave a full update to a separate issue.

Hints from issue discussion:
There's a PR outstanding for this (see above #2061) but we are blocked on a bug in the example code (explained on Slack https://github.com/kedro-org/kedro-viz/issues/1205) and a bug in Kedro Viz (https://github.com/kedro-org/kedro-viz/issues/1205)

Commit message: Reduce diagram width

Patch to review:
diff --git a/docs/source/contribution/contribute_to_kedro.md b/docs/source/contribution/contribute_to_kedro.md
index 51a1aa594d..2695589711 100644
--- a/docs/source/contribution/contribute_to_kedro.md
+++ b/docs/source/contribution/contribute_to_kedro.md
@@ -5,7 +5,7 @@ We welcome any and all contributions to Kedro, at whatever level you can manage.
 - Join the community on [Slack](https://slack.kedro.org)
 - Review Kedro's [GitHub issues](https://github.com/kedro-org/kedro/issues) or raise your own issue to report a bug or feature request
 - Start a conversation about the Kedro project on [GitHub discussions](https://github.com/kedro-org/kedro/discussions)
-- Make a pull request on the [Kedro-Community GitHub repo](https://github.com/kedro-org/kedro-community) to update the curated list of Kedro community content.
+- Make a pull request on the [Kedro-Community GitHub repo](https://github.com/kedro-org/kedro-community) to update the curated list of Kedro community content
 - Report a bug or propose a new feature on [GitHub issues](https://github.com/kedro-org/kedro/issues)
 - [Review other contributors' PRs](https://github.com/kedro-org/kedro/pulls)
 - [Contribute code](./developer_contributor_guidelines.md), for example to fix a bug or add a feature
diff --git a/docs/source/meta/images/chart-icon.png b/docs/source/meta/images/chart-icon.png
index a3836fbcfa..109ef9fb71 100644
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diff --git a/docs/source/meta/images/pipeline_visualisation_matplotlib.png b/docs/source/meta/images/pipeline_visualisation_matplotlib.png
index 30d26d099b..89645a8e4d 100644
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diff --git a/docs/source/meta/images/pipeline_visualisation_matplotlib_expand.png b/docs/source/meta/images/pipeline_visualisation_matplotlib_expand.png
index 2928399efd..54fe95194c 100644
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diff --git a/docs/source/meta/images/pipeline_visualisation_plotly_1.png b/docs/source/meta/images/pipeline_visualisation_plotly_1.png
new file mode 100644
index 0000000000..32c3937517
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diff --git a/docs/source/meta/images/pipeline_visualisation_plotly_expand_1.png b/docs/source/meta/images/pipeline_visualisation_plotly_expand_1.png
new file mode 100644
index 0000000000..13e36da43e
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diff --git a/docs/source/visualisation/experiment_tracking.md b/docs/source/visualisation/experiment_tracking.md
index 0f0bccae7c..899668f8b3 100644
--- a/docs/source/visualisation/experiment_tracking.md
+++ b/docs/source/visualisation/experiment_tracking.md
@@ -1,31 +1,33 @@
 # Set up experiment tracking
 
-Experiment tracking is the process of saving all machine-learning related experiment information so that it is easy to find and compare past runs. [Kedro-Viz](https://github.com/kedro-org/kedro-viz) supports native experiment tracking from [version 4.1.1](https://github.com/kedro-org/kedro-viz/releases/tag/v4.1.1) onwards. When experiment tracking is enabled in your Kedro project, you will be able to access, edit and [compare your experiments](#access-run-data-and-compare-runs) directly from the Kedro-Viz web app, as well as [see how your metrics have changed over time](#view-your-metrics-timeline).
+Experiment tracking is the process of saving all machine-learning related experiment information so you can compare different runs.
+
+[Kedro-Viz](https://github.com/kedro-org/kedro-viz) supports native experiment tracking from [version 4.1.1](https://github.com/kedro-org/kedro-viz/releases/tag/v4.1.1) onwards. When experiment tracking is enabled in your Kedro project, you will be able to access, edit and [compare your experiments](#access-run-data-and-compare-runs) and additionally [track how your metrics change over time](#view-your-metrics-timeline).
 
 ![](../meta/images/experiment-tracking_demo_small.gif)
 
 Enabling experiment tracking features on Kedro-Viz relies on:
+
 * [setting up a session store to capture experiment metadata](#set-up-the-session-store),
 * [experiment tracking datasets to let Kedro know what metrics should be tracked](#set-up-tracking-datasets)
 * [modifying your nodes and pipelines to output those metrics](#set-up-your-nodes-and-pipelines-to-log-metrics).
 
-This tutorial will provide a step-by-step process to set up experiment tracking and access your logged metrics from each run on Kedro-Viz. It will use the starter outlined in the [spaceflights tutorial](../tutorial/spaceflights_tutorial.md). You can also jump directly to [this section for direct reference in setting up experiment tracking](../logging/experiment_tracking.md) for your Kedro project.
+This page describes the steps necessary to set up experiment tracking and access logged metrics, using the [spaceflights tutorial](../tutorial/spaceflights_tutorial.md).
+
+
+[Further information is available in the experiment tracking section](../logging/experiment_tracking.md) for your Kedro project.
 
 You can also access a more detailed [Kedro-Viz live demo](https://kedro-viz-live-demo.hfa4c8ufrmn4u.eu-west-2.cs.amazonlightsail.com/).
 
 ## Set up a project
 
-```{note}
-You can skip this step if you have followed all previous parts of the tutorial.
-```
-
 We assume that you have already [installed Kedro](../get_started/install.md) and [Kedro-Viz](../visualisation/kedro-viz_visualisation.md). To set up a new project using the spaceflights starter, run:
 
 ```bash
 kedro new --starter=spaceflights
 ```
 
-Feel free to name your project as you like, but this guide will assume the project is named **Kedro Experiment Tracking Tutorial**, and that your project is in a sub-folder in your working directory that was created by `kedro new`, named `kedro-experiment-tracking-tutorial`.
+Feel free to name your project as you like, but this guide will assume the project is named **Kedro Experiment Tracking Tutorial**.
 
 ## Set up the session store
 
@@ -142,7 +144,7 @@ node(
 
 Having set up both datasets, you can now generate your first set of experiment tracking data!
 
-## Generate the Run data
+## Generate the run data
 
 The beauty of native experiment tracking in Kedro is that all tracked data is generated and stored each time you do a Kedro run. Hence, to generate the data, you need only execute:
 
@@ -225,8 +227,6 @@ After running the pipeline with `kedro run`, the plot will be saved and you will
 ![](../meta/images/expand-plot-comparison-view.gif)
 
 
-> [Read more about creating plots and visualising them in Kedro-Viz](./kedro-viz_visualisation.md).
-
 ## View your metrics timeline
 
 Additionally, you can monitor the changes to metrics over time from the pipeline visualisation tab ![](../meta/images/pipeline_visualisation_icon.png). Clicking on any MetricsDataset node will open a side panel displaying how the metric value has changed over time.
diff --git a/docs/source/visualisation/kedro-viz_visualisation.md b/docs/source/visualisation/kedro-viz_visualisation.md
index 01eba1ffd9..c9ebbc8d31 100644
--- a/docs/source/visualisation/kedro-viz_visualisation.md
+++ b/docs/source/visualisation/kedro-viz_visualisation.md
@@ -1,6 +1,6 @@
 # Get started with Kedro-Viz
 
-[Kedro-Viz](https://github.com/kedro-org/kedro-viz) is a key part of Kedro. It displays data and nodes, and the connections between them, to visualise the structure of the pipelines in a Kedro project.
+[Kedro-Viz](https://github.com/kedro-org/kedro-viz) is a key part of Kedro. It visualises the pipelines in a Kedro project by showing data, nodes, and the connections between them.
 
 This section assumes you are familiar with the basic Kedro concepts described in the [spaceflights tutorial](../tutorial/spaceflights_tutorial.md). If you have not yet worked through the tutorial, you can still follow this example.
 
@@ -18,15 +18,21 @@ When your project is ready, navigate to the root directory of the project and in
 pip install -r src/requirements.txt
 ```
 
+Then run the full set of pipelines for the tutorial project:
+
+```bash
+kedro run
+```
+
 ## Visualise the spaceflights project
 
-To run Kedro-Viz, type the following into your terminal from the project directory:
+To start Kedro-Viz, type the following into your terminal from the project directory:
 
 ```bash
 kedro viz
 ```
 
-The command automatically opens a browser tab to serve the visualisation at http://127.0.0.1:4141/.
+The command automatically opens a browser tab to serve the visualisation at `http://127.0.0.1:4141/`.
 
 You should see the following:
 
@@ -114,7 +120,7 @@ The visualisation now includes the layers:
 
 ## Share a pipeline visualisation
 
-You can share a Kedro-Viz visualisation as a JSON file from the terminal:
+You can share a Kedro-Viz visualisation as a JSON file from the terminal. :
 
 ```bash
 kedro viz --save-file my_shareable_pipeline.json
diff --git a/docs/source/visualisation/visualise_charts_with_plotly.md b/docs/source/visualisation/visualise_charts_with_plotly.md
index a75b46545a..09c99a7ae8 100644
--- a/docs/source/visualisation/visualise_charts_with_plotly.md
+++ b/docs/source/visualisation/visualise_charts_with_plotly.md
@@ -1,60 +1,53 @@
 # Visualise charts in Kedro-Viz
 
-## Visualisation with Plotly
-Kedro-Viz supports [Plotly](https://plotly.com/python/) and [Matplotlib](https://matplotlib.org/) integrations so that you can share your data insights while exploring your pipeline.
-
-[Plotly](https://plotly.com/python/) is a free and open source Python library that allows you to make interactive, publication-quality graphs. With the Plotly integration on Kedro-Viz, you can output your interactive charts as part of your pipeline visualisation.
-
-We have also used the Plotly integration to allow users to [visualise metrics from experiments](../logging/experiment_tracking.md).
+This page describes how to make interactive visualisations of your Kedro projects with Kedro-Viz, which supports integration with [Plotly](https://plotly.com/python/) and [Matplotlib](https://matplotlib.org/).
 
+## Visualisation with Plotly
 
-You must update the `requirements.txt` file in your Kedro project and add the following datasets to enable Plotly for your project.
+We use the [spaceflights tutorial](../tutorial/spaceflights_tutorial.md) and add a reporting pipeline that uses Plotly. Even if you have not yet worked through the tutorial, you can still follow this example; you'll need to use the [Kedro starter for the spaceflights tutorial](https://github.com/kedro-org/kedro-starters/tree/main/spaceflights) to generate a copy of the project with working code in place:
 
-`kedro-datasets[plotly.PlotlyDataSet, plotly.JSONDataSet]~=1.0.0`
+From your terminal:
 
+```bash
+kedro new --starter=spaceflights
+```
 
-You can view Plotly charts in Kedro-Viz when you use Kedro's plotly datasets.
+When prompted for a project name, you can enter any name, but we will assume `Kedro Tutorial` throughout.
 
-There are two types of Plotly datasets in Kedro: the `plotly.PlotlyDataSet` and `plotly.JSONDataSet`.
+### Update the dependencies
 
-### `plotly.PlotlyDataSet`
+There are two types of Plotly datasets supported by Kedro:
 
-To use this dataset, configure your plot in the `catalog.yml` file. This dataset only supports [Plotly Express](https://plotly.com/python/plotly-express).
+* `plotly.PlotlyDataSet` which only supports [Plotly Express](https://plotly.com/python/plotly-express)
+* `plotly.JSONDataSet` which supports Plotly Express and [Plotly Graph Objects](https://plotly.com/python/graph-objects/)
 
-Below is an example of how to visualise plots on Kedro-Viz using `plotly.PlotlyDataSet`.
+To use the Plotly datasets, you must update the `requirements.txt` file in the `src` folder of your Kedro project to add the following dependencies:
 
-The below functions can be added to the `nodes.py` and `pipeline.py` files respectively.
+```{note}
+At the time of writing, the tutorial works up to Kedro version 0.18.3 so you need to pin the versions as shown.
+```
 
-```python
-# nodes.py
-import pandas as pd
+```text
+kedro==0.18.3
+kedro[pandas.CSVDataSet, pandas.ExcelDataSet, pandas.ParquetDataSet]==0.18.3
+kedro[plotly.PlotlyDataSet, plotly.JSONDataSet]==0.18.3
+```
 
+Navigate to the root directory of the project in your terminal and install the dependencies for the tutorial project:
 
-def compare_passenger_capacity(preprocessed_shuttles: pd.DataFrame):
-    return preprocessed_shuttles.groupby(["shuttle_type"]).mean().reset_index()
+```bash
+pip install -r src/requirements.txt
 ```
 
-```python
-# pipeline.py
-def create_pipeline(**kwargs) -> Pipeline:
-    """This is a simple pipeline which generates a plot"""
-    return pipeline(
-        [
-            node(
-                func=compare_passenger_capacity,
-                inputs="preprocessed_shuttles",
-                outputs="shuttle_passenger_capacity_plot",
-            ),
-        ]
-    )
-```
+### Configure the Data Catalog
 
-Next, configure the plot in the `catalog.yml` file:
+To use the datasets, add them to the Data Catalog by updating `conf/base/catalog.yml`:
 
 ```yaml
-shuttle_passenger_capacity_plot:
+shuttle_passenger_capacity_plot_exp:
   type: plotly.PlotlyDataSet
-  filepath: data/08_reporting/shuttle_passenger_capacity_plot.json
+  filepath: data/08_reporting/shuttle_passenger_capacity_plot_exp.json
+  versioned: true
   plotly_args:
     type: bar
     fig:
@@ -65,36 +58,39 @@ shuttle_passenger_capacity_plot:
       xaxis_title: Shuttles
       yaxis_title: Average passenger capacity
       title: Shuttle Passenger capacity
+
+shuttle_passenger_capacity_plot_go:
+  type: plotly.JSONDataSet
+  filepath: data/08_reporting/shuttle_passenger_capacity_plot_go.json
+  versioned: true
 ```
 
 
-### `plotly.JSONDataSet`
+### Create the template reporting pipeline
 
-To use this dataset, configure your plot in your Kedro node. This dataset supports [Plotly Express](https://plotly.com/python/plotly-express) and [Plotly Graph Objects](https://plotly.com/python/graph-objects/).
+In the terminal, run the following command to generate a template for the reporting pipeline:
 
+```bash
+kedro pipeline create reporting
+```
 
-Below is an example of how to visualise plots using [Plotly Express](https://plotly.com/python/plotly-express) and [Plotly Graph Objects](https://plotly.com/python/graph-objects/) on Kedro-Viz using the `plotly.JSONDataSet`.
+### Add the Plotly reporting nodes
 
-The below functions can be added to the `nodes.py` and `pipeline.py` files respectively.
+Add the following to `src/kedro_tutorial/pipelines/reporting/nodes.py`:
 
 ```python
-# nodes.py
 import plotly.express as px
 import plotly.graph_objs as go
 import pandas as pd
 
-# the below function uses plotly.express
-def compare_passenger_capacity(preprocessed_shuttles: pd.DataFrame):
-    fig = px.bar(
-        data_frame=preprocessed_shuttles.groupby(["shuttle_type"]).mean().reset_index(),
-        x="shuttle_type",
-        y="passenger_capacity",
-    )
-    return fig
+# This function uses plotly.express
+def compare_passenger_capacity_exp(preprocessed_shuttles: pd.DataFrame):
+    return preprocessed_shuttles.groupby(["shuttle_type"]).mean().reset_index()
 
 
-# the below function uses plotly.graph_objects
-def compare_passenger_capacity(preprocessed_shuttles: pd.DataFrame):
+# This function uses plotly.graph_objects
+def compare_passenger_capacity_go(preprocessed_shuttles: pd.DataFrame):
+
     data_frame = preprocessed_shuttles.groupby(["shuttle_type"]).mean().reset_index()
     fig = go.Figure(
         [
@@ -104,67 +100,96 @@ def compare_passenger_capacity(preprocessed_shuttles: pd.DataFrame):
             )
         ]
     )
+
     return fig
 ```
 
+### Update the reporting pipeline code
+
+Update `src/kedro_tutorial/pipelines/reporting/pipeline.py` to replace the existing code with the following:
+
 ```python
-# pipeline.py
+from kedro.pipeline import Pipeline, node, pipeline
+from .nodes import compare_passenger_capacity_exp, compare_passenger_capacity_go
+
 def create_pipeline(**kwargs) -> Pipeline:
-    """This is a simple pipeline which generates a plot"""
+    """This is a simple pipeline which generates a pair of plots"""
     return pipeline(
         [
             node(
-                func=compare_passenger_capacity,
+                func=compare_passenger_capacity_exp,
+                inputs="preprocessed_shuttles",
+                outputs="shuttle_passenger_capacity_plot_exp",
+            ),
+            node(
+                func=compare_passenger_capacity_go,
                 inputs="preprocessed_shuttles",
-                outputs="shuttle_passenger_capacity_plot",
+                outputs="shuttle_passenger_capacity_plot_go",
             ),
         ]
-    )
-```
+    )```
 
-For `plotly.JSONDataSet`, you must also specify the output type in the `catalog.yml` file, like below.
 
-```yaml
-shuttle_passenger_capacity_plot:
-  type: plotly.JSONDataSet
-  filepath: data/08_reporting/shuttle_passenger_capacity_plot.json
-```
+### Run the pipeline
 
+Now run the pipelines:
 
+```bash
+kedro run
+```
 
-Once the above setup is completed, you can do a `kedro run` followed by `kedro viz` and your Kedro-Viz pipeline will show a new dataset type with icon ![](../meta/images/chart-icon.png) . Click on the node to see a small preview of your Plotly chart in the metadata panel.
+Then visualise with `kedro viz`
 
-![](../meta/images/pipeline_visualisation_plotly.png)
+The generated charts are shown as follows:
 
+![](../meta/images/chart-icon.png).
 
+Click on each of see a small preview in the metadata panel:
 
-You can view the larger visualisation of the chart by clicking the 'Expand Plotly Visualisation' button on the bottom of the metadata panel.
+![](../meta/images/pipeline_visualisation_plotly_1.png)
 
+View the larger visualisation of the chart by clicking the 'Expand Plotly Visualisation' button on the bottom of the metadata panel.
 
-![](../meta/images/pipeline_visualisation_plotly_expand.png)
+![](../meta/images/pipeline_visualisation_plotly_expand_1.png)
 
 
-### Visualisation with Matplotlib
+## Visualisation with Matplotlib
 
-[Matplotlib](https://matplotlib.org/) is a Python library for creating static, animated, and interactive visualisations. Integrating Matplotlib into Kedro-Viz allows you to output your charts as part of your pipeline visualisation.
+Integrating Matplotlib into Kedro-Viz allows you to output charts as part of your pipeline visualisation.
 
 ```{note}
 The MatplotlibWriter dataset converts Matplotlib objects to image files. This means that Matplotlib charts within Kedro-Viz are static and not interactive, unlike the Plotly charts seen above.
 ```
 
-You can view Matplotlib charts in Kedro-Viz when you use the [Kedro MatplotLibWriter dataset](/kedro.extras.datasets.matplotlib.MatplotlibWriter). You must update the `src/requirements.txt` file in your Kedro project by adding the following dataset to enable Matplotlib for your project:
+You can view Matplotlib charts in Kedro-Viz when you use the [Kedro MatplotLibWriter dataset](/kedro.datasets.matplotlib.MatplotlibWriter).
+
+### Update the dependencies
+
+You must update the `src/requirements.txt` file in your Kedro project by adding the following dataset to enable Matplotlib for your project:
 
+```bash
+kedro[matplotlib.MatplotlibWriter]==0.18.3
 ```
-kedro-datasets[matplotlib.MatplotlibWriter]~=1.0.0
+
+### Configure the Data Catalog
+You must also specify the output type in the `catalog.yml` file for the Data Catalog:
+
+```yaml
+dummy_confusion_matrix:
+  type: matplotlib.MatplotlibWriter
+  filepath: data/08_reporting/dummy_confusion_matrix.png
+  versioned: true
 ```
 
-To use this dataset, configure your plot in your Kedro node. The below functions should be added to the `nodes.py` and `pipeline.py` files respectively.
+### Add another node
+Add the following to `src/kedro_tutorial/pipelines/reporting/nodes.py`:
 
 ```python
-# nodes.py
 import matplotlib.pyplot as plt
 import seaborn as sn
 
+...
+
 
 def create_confusion_matrix(companies: pd.DataFrame):
     actuals = [0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1]
@@ -178,10 +203,20 @@ def create_confusion_matrix(companies: pd.DataFrame):
     return plt
 ```
 
-> You might have to execute `pip install seaborn` if the [seaborn library](https://seaborn.pydata.org/) is not installed yet.
+``` {note}
+You might have to execute `pip install seaborn` if the [seaborn library](https://seaborn.pydata.org/) is not installed yet.
+```
+
+### Update the pipeline
+
+Update `src/kedro_tutorial/pipelines/reporting/pipeline.py` to add the following to `create_pipeline`:
 
 ```python
-# pipeline.py
+from .nodes import create_confusion_matrix
+
+...
+
+
 def create_pipeline(**kwargs) -> Pipeline:
     """This is a simple pipeline which generates a plot"""
     return pipeline(
@@ -195,19 +230,14 @@ def create_pipeline(**kwargs) -> Pipeline:
     )
 ```
 
-You must also specify the output type in the `catalog.yml` file, like below. Remember to set the versioned flag to `true` if you want to add the plots to experiment tracking as well.
+### Run the pipeline
 
-```yaml
-dummy_confusion_matrix:
-  type: matplotlib.MatplotlibWriter
-  filepath: data/08_reporting/dummy_confusion_matrix.png
-  versioned: true
-```
+Run the pipelines with `kedro run` and then visualise the result with `kedro viz`.
 
-Once this setup is completed, you can do a `kedro run` followed by `kedro viz` and your Kedro-Viz pipeline will show a new dataset node with this icon ![](../meta/images/chart-icon.png). Click on the node to see a small preview of your Matplotlib image in the metadata panel.
+Click to see a small preview of your Matplotlib image in the metadata panel.
 
 ![](../meta/images/pipeline_visualisation_matplotlib.png)
 
-You can view the larger visualisation of the chart by clicking the 'Expand Matplotlib Image' button on the bottom of the metadata panel.
+View the larger visualisation of the chart by clicking the 'Expand Matplotlib Image' button on the bottom of the metadata panel.
 
 ![](../meta/images/pipeline_visualisation_matplotlib_expand.png)


Code review comments:
diff_hunk: @@ -1,31 +1,33 @@
 # Set up experiment tracking
 
-Experiment tracking is the process of saving all machine-learning related experiment information so that it is easy to find and compare past runs. [Kedro-Viz](https://github.com/kedro-org/kedro-viz) supports native experiment tracking from [version 4.1.1](https://github.com/kedro-org/kedro-viz/releases/tag/v4.1.1) onwards. When experiment tracking is enabled in your Kedro project, you will be able to access, edit and [compare your experiments](#access-run-data-and-compare-runs) directly from the Kedro-Viz web app, as well as [see how your metrics have changed over time](#view-your-metrics-timeline).
+Experiment tracking is the process of saving all machine-learning related experiment information so you can compare different runs.
+
+[Kedro-Viz](https://github.com/kedro-org/kedro-viz) supports native experiment tracking from [version 4.1.1](https://github.com/kedro-org/kedro-viz/releases/tag/v4.1.1) onwards. When experiment tracking is enabled in your Kedro project, you will be able to access, edit and [compare your experiments](#access-run-data-and-compare-runs) and additionally [track how your metrics change over time](#view-your-metrics-timeline).
 
 ![](../meta/images/experiment-tracking_demo_small.gif)
 
 Enabling experiment tracking features on Kedro-Viz relies on:
+
 * [setting up a session store to capture experiment metadata](#set-up-the-session-store),
 * [experiment tracking datasets to let Kedro know what metrics should be tracked](#set-up-tracking-datasets)
 * [modifying your nodes and pipelines to output those metrics](#set-up-your-nodes-and-pipelines-to-log-metrics).
 
-This tutorial will provide a step-by-step process to set up experiment tracking and access your logged metrics from each run on Kedro-Viz. It will use the starter outlined in the [spaceflights tutorial](../tutorial/spaceflights_tutorial.md). You can also jump directly to [this section for direct reference in setting up experiment tracking](../logging/experiment_tracking.md) for your Kedro project.
+This page describes the steps necessary to set up experiment tracking and access logged metrics, using the [spaceflights tutorial](../tutorial/spaceflights_tutorial.md).
+
+
+[Further information is available in the experiment tracking section](../logging/experiment_tracking.md) for your Kedro project.
 
 You can also access a more detailed [Kedro-Viz live demo](https://kedro-viz-live-demo.hfa4c8ufrmn4u.eu-west-2.cs.amazonlightsail.com/).
path: docs/source/visualisation/experiment_tracking.md
text: @user1:
```suggestion
You can also access a more detailed [Kedro-Viz experiment tracking demo](https://demo.kedro.org/experiment-tracking).
```

@user1:
I'm also wondering we should include the demo link in the "Get Started with Kedro-Viz" section? It would be nice.