[GitHub] [beam] damccorm commented on a diff in pull request #23887: Add notebook for doing remote inference in Beam

[GitBox <gi...@apache.org>]

damccorm commented on code in PR #23887:
URL: https://github.com/apache/beam/pull/23887#discussion_r1010613260


##########
website/www/site/content/en/documentation/ml/overview.md:
##########
@@ -47,12 +47,26 @@ Further reading:
 
 ## Inference
 
-There are several ways to use and deploy your model:
-1. Making it available for online predictions via an API
-2. Running it in real-time as new data becomes available in a pipeline
-3. Running it in batch on an existing dataset
+Beam provides different ways of implementing inference as part of your pipeline. This way you can run your ML model directly in your pipeline and apply it on big scale datasets, both in batch and streaming pipelines.
+
+### RunInference
+The recommended way to implement inference is by using the [RunInference API](https://beam.apache.org/documentation/sdks/python-machine-learning/). RunInference takes advantage of existing Apache Beam concepts, such as the `BatchElements` transform and the `Shared` class, to enable you to use models in your pipelines to create transforms optimized for machine learning inferences. The ability to create arbitrarily complex workflow graphs also allows you to build multi-model pipelines.
+
+You can easily integrate your model in your pipeline by using the corresponding model handlers. A `ModelHandler` is an object that wraps the underlying model and allows you to configure its parameters. Model handlers are available for PyTorch, Scikit-learn and TensorFlow. Examples of how to use RunInference for PyTorch, Scikit-learn and TensorFlow are shown in this [notebook](https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_pytorch_tensorflow_sklearn.ipynb).
+
+GPUs are optimized for training artificial intelligence and deep learning models as they can process multiple computations simultaneously. RunInference also allows you to use GPUs for significant inference speedup. An example of how to use RunInference with GPUs is demonstrated[here](/documentation/ml/runinference-metrics).

Review Comment:
   ```suggestion
   GPUs are optimized for training artificial intelligence and deep learning models as they can process multiple computations simultaneously. RunInference also allows you to use GPUs for significant inference speedup. An example of how to use RunInference with GPUs is demonstrated [here](/documentation/ml/runinference-metrics).
   ```



-- 
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.

To unsubscribe, e-mail: github-unsubscribe@beam.apache.org

For queries about this service, please contact Infrastructure at:
users@infra.apache.org