Add demo of using custom visualization with taxi pipeline (#1533)

* Add example of using custom visualization with taxi pipeline

* fix a typo

content/docs/pipelines/sdk/python-based-visualizations.md

@@ -119,6 +119,32 @@ optional for custom visualizations).
 8. Click **Generate Visualization**.
 9. View generated visualization by scrolling down.
 
+A demo of the above instructions is as follows.
+
+<video width="100%" max-width="100%" height="auto" max-height="100%" controls>
+  <source src="/docs/videos/taxi_custom_visualization.webm" type="video/webm">
+</video>
+
+1. On the Pipelines page, click **[Sample] Unified DSL - Taxi Tip Prediction Model Trainer** to open the Pipeline Details page.
+2. On the Pipeline Details page, click **Create run**.
+3. On the Create run page,
+    * Use a run name and an experiment name of your choice or simply use the default names choosen for you.
+    * In the **pipeline-root** field, specify a storage bucket that you have permission to write to. For example, enter the path to a Google Cloud Storage bucket or an Amazon S3 bucket.
+    * Click **Start** to create the run.
+4. After the run is complete, on the Run Details page, click the step of **statisticsgen**. This step's output is statistics data generated by Tensorflow Data Validation.
+5. In the side panel of the selected step,
+    * Click the **Input/Output** tab to find out the **mlpipeline-ui-metadata** item and click the minio link there. This will open a new browser tab with information on output file path. Copy the output file path as shown in the demo video.
+    * Get back to the Run Details page, and click the **Artifacts** tab.
+    * At the top of the tab you should see a card named **Visualization Creator**, choose **Custom** from the drop down menu.
+    * In the **Custom Visualization Code** field, fill in the following code snippet and replace [output file path] with the output file path you just copied from **mlpipeline-ui-metadata**.
+    <pre style="overflow-x:scroll;overflow-y:hidden;white-space:pre;">
+      import tensorflow_data_validation as tfdv
+      stats = tfdv.load_statistics('[output file path]/stats_tfrecord')
+      tfdv.visualize_statistics(stats)
+    </pre>
+    * Click **Generate Visualization** and wait.
+6. Move to the bottom of the **Artifacts** tab to find the generated visualization.
+
 ## Known limitations
 * Multiple visualizations cannot be generated concurrently.
     * This is because a single Python kernel is used to generate visualizations.