[GitHub] [beam] damccorm commented on a diff in pull request #26285: Adds a reference to new Java RunInference example

["damccorm (via GitHub)" <gi...@apache.org>]

damccorm commented on code in PR #26285:
URL: https://github.com/apache/beam/pull/26285#discussion_r1167226283


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website/www/site/content/en/documentation/sdks/python-machine-learning.md:
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@@ -245,8 +245,7 @@ For detailed instructions explaining how to build and run a Python pipeline that
 
 ## Beam Java SDK support
 
-The RunInference API is available with the Beam Java SDK versions 2.41.0 and later through Apache Beam's [Multi-language Pipelines framework](/documentation/programming-guide/#multi-language-pipelines). For information about the Java wrapper transform, see [RunInference.java](https://github.com/apache/beam/blob/master/sdks/java/extensions/python/src/main/java/org/apache/beam/sdk/extensions/python/transforms/RunInference.java). To try it out, see the [Java Sklearn Mnist Classification example](https://github.com/apache/beam/tree/master/examples/multi-language).
-
+The RunInference API is available with the Beam Java SDK versions 2.41.0 and later through Apache Beam's [Multi-language Pipelines framework](/documentation/programming-guide/#multi-language-pipelines). For information about the Java wrapper transform, see [RunInference.java](https://github.com/apache/beam/blob/master/sdks/java/extensions/python/src/main/java/org/apache/beam/sdk/extensions/python/transforms/RunInference.java). To try it out, see the [Java Sklearn Mnist Classification example](https://github.com/apache/beam/tree/master/examples/multi-language). Additionally, see [Using RunInference from Java SDK](https://beam.apache.org/documentation/ml/multi-language-inference/) for an example that uses a composite Python transform that uses the RunInference API along with preprocessing and postprocessing from a Beam Java SDK pipeline.

Review Comment:
   ```suggestion
   The RunInference API is available with the Beam Java SDK versions 2.41.0 and later through Apache Beam's [Multi-language Pipelines framework](/documentation/programming-guide/#multi-language-pipelines). For information about the Java wrapper transform, see [RunInference.java](https://github.com/apache/beam/blob/master/sdks/java/extensions/python/src/main/java/org/apache/beam/sdk/extensions/python/transforms/RunInference.java). To try it out, see the [Java Sklearn Mnist Classification example](https://github.com/apache/beam/tree/master/examples/multi-language). Additionally, see [Using RunInference from Java SDK](https://beam.apache.org/documentation/ml/multi-language-inference/) for an example of a composite Python transform that uses the RunInference API along with preprocessing and postprocessing from a Beam Java SDK pipeline.
   ```
   
   optional nit: I think this reads better because it avoids repeating "that uses" (`X that uses Y that uses Z`)



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