[beam] branch master updated: Add links to the new RunInference content to Learning Resources (#22325)

[tv...@apache.org]

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tvalentyn pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/beam.git


The following commit(s) were added to refs/heads/master by this push:
     new 1abfb547bb3 Add links to the new RunInference content to Learning Resources (#22325)
1abfb547bb3 is described below

commit 1abfb547bb378670fef2d9aedbfdcfb67db01637
Author: Rebecca Szper <98...@users.noreply.github.com>
AuthorDate: Tue Jul 19 07:30:40 2022 -0700

    Add links to the new RunInference content to Learning Resources (#22325)
    
    Co-authored-by: tvalentyn <tv...@users.noreply.github.com>
---
 .../www/site/content/en/documentation/resources/learning-resources.md    | 1 +
 1 file changed, 1 insertion(+)

diff --git a/website/www/site/content/en/documentation/resources/learning-resources.md b/website/www/site/content/en/documentation/resources/learning-resources.md
index 4be5b50aece..6c681ce4589 100644
--- a/website/www/site/content/en/documentation/resources/learning-resources.md
+++ b/website/www/site/content/en/documentation/resources/learning-resources.md
@@ -66,6 +66,7 @@ If you have additional material that you would like to see here, please let us k
 
 ### Machine Learning
 
+*   **[Machine Learning with Python using the RunInference API](/documentation/sdks/python-machine-learning/)** - Use Apache Beam with the RunInference API to use machine learning (ML) models to do local and remote inference with batch and streaming pipelines. Follow the [RunInference API pipeline examples](https://github.com/apache/beam/tree/master/sdks/python/apache_beam/examples/inference) to do image classification, image segmentation, language modeling, and MNIST digit classificatio [...]
 *   **[Machine Learning Preprocessing and Prediction](https://cloud.google.com/dataflow/examples/molecules-walkthrough)** - Predict the molecular energy from data stored in the [Spatial Data File](https://en.wikipedia.org/wiki/Spatial_Data_File) (SDF) format. Train a [TensorFlow](https://www.tensorflow.org/) model with [tf.Transform](https://github.com/tensorflow/transform) for preprocessing in Python. This also shows how to create batch and streaming prediction pipelines in Apache Beam.
 *   **[Machine Learning Preprocessing](https://cloud.google.com/blog/products/ai-machine-learning/pre-processing-tensorflow-pipelines-tftransform-google-cloud)** - Find the optimal parameter settings for simulated physical machines like a bottle filler or cookie machine. The goal of each simulated machine is to have the same input/output of the actual machine, making it a "digital twin". This uses [tf.Transform](https://github.com/tensorflow/transform) for preprocessing.