[beam] 01/01: Embed ML video to docs

[da...@apache.org]

This is an automated email from the ASF dual-hosted git repository.

damccorm pushed a commit to branch users/damccorm/embedVideo
in repository https://gitbox.apache.org/repos/asf/beam.git

commit 9d8c73566b5cd1bfab75082cc692a7024f6a338b
Author: Danny McCormick <da...@google.com>
AuthorDate: Fri Feb 3 11:26:53 2023 -0500

    Embed ML video to docs
---
 website/www/site/content/en/documentation/ml/overview.md | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/website/www/site/content/en/documentation/ml/overview.md b/website/www/site/content/en/documentation/ml/overview.md
index 37038e7e108..3b6b6eac020 100644
--- a/website/www/site/content/en/documentation/ml/overview.md
+++ b/website/www/site/content/en/documentation/ml/overview.md
@@ -24,6 +24,10 @@ Beam <3 machine learning. Being productive and successful as a machine learning
   upscaling your data pipelines as part of your MLOps ecosystem in a production environment.
 * It enables you to run your model in production on a varying data load, both in batch and streaming.
 
+<br><br>
+<iframe class="video video--medium-size" width="560" height="315" src="[https://www.youtube.com/embed/H4s7rAlk68w](https://www.youtube.com/watch?v=ga2TNdrFRoU)" frameborder="0" allowfullscreen></iframe>
+<br><br>
+
 ## AI/ML workloads
 
 Let’s take a look at the different building blocks that we need to create an end-to-end AI/ML use case and where Apache Beam can help.
@@ -91,4 +95,4 @@ You can find examples of end-to-end AI/ML pipelines for several use cases:
 * [Online Clustering in Beam](/documentation/ml/online-clustering): Demonstrates how to set up a real-time clustering pipeline that can read text from Pub/Sub, convert the text into an embedding using a transformer-based language model with the RunInference API, and cluster the text using BIRCH with stateful processing.
 * [Anomaly Detection in Beam](/documentation/ml/anomaly-detection): Demonstrates how to set up an anomaly detection pipeline that reads text from Pub/Sub in real time and then detects anomalies using a trained HDBSCAN clustering model with the RunInference API.
 * [Large Language Model Inference in Beam](/documentation/ml/large-language-modeling): Demonstrates a pipeline that uses RunInference to perform translation with the T5 language model which contains 11 billion parameters.
-* [Per Entity Training in Beam](/documentation/ml/per-entity-training): Demonstrates a pipeline that trains a Decision Tree Classifier per education level for predicting if the salary of a person is >= 50k.
\ No newline at end of file
+* [Per Entity Training in Beam](/documentation/ml/per-entity-training): Demonstrates a pipeline that trains a Decision Tree Classifier per education level for predicting if the salary of a person is >= 50k.