[beam] branch master updated: Add brief descriptions about end-to-end ML Pipelines (#23880)

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The following commit(s) were added to refs/heads/master by this push:
     new abb932e97ab Add brief descriptions about end-to-end ML Pipelines (#23880)
abb932e97ab is described below

commit abb932e97ab2e87ec1eaf0dbf9f177b949a35751
Author: Shubham Krishna <sh...@gmail.com>
AuthorDate: Mon Oct 31 13:50:16 2022 +0100

    Add brief descriptions about end-to-end ML Pipelines (#23880)
    
    * Add brief descriptions about end-to-end ML Pipelines
    
    * Fix wording and small nitpicks
    
    Co-authored-by: Shubham Krishna <“shubham.krishna@ml6.eu”>
---
 website/www/site/content/en/documentation/ml/overview.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/website/www/site/content/en/documentation/ml/overview.md b/website/www/site/content/en/documentation/ml/overview.md
index 0423686329c..6917a2fec3d 100755
--- a/website/www/site/content/en/documentation/ml/overview.md
+++ b/website/www/site/content/en/documentation/ml/overview.md
@@ -61,6 +61,6 @@ In order to automate and track the AI/ML workflows throughout your project, you
 ## Examples
 
 You can find examples of end-to-end AI/ML pipelines for several use cases:
-* [Multi model pipelines in Beam](/documentation/ml/multi-model-pipelines)
-* [Online Clustering in Beam](/documentation/ml/online-clustering)
-* [Anomaly Detection in Beam](/documentation/ml/anomaly-detection)
+* [Multi model pipelines in Beam](/documentation/ml/multi-model-pipelines): explains how multi-model pipelines work and gives an overview of what you need to know to build one using the RunInference API.
+* [Online Clustering in Beam](/documentation/ml/online-clustering): demonstrates how to setup a realtime clustering pipeline that can read text from PubSub, convert the text into an embedding using a transformer based language model with the RunInference API, and cluster them using BIRCH with Stateful Processing.
+* [Anomaly Detection in Beam](/documentation/ml/anomaly-detection): demonstrates how to setup an anomaly detection pipeline that reads text from PubSub in real-time, and then detects anomaly using a trained HDBSCAN clustering model with the RunInference API.