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

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

shub-kris commented on code in PR #23887:
URL: https://github.com/apache/beam/pull/23887#discussion_r1010727725


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website/www/site/content/en/documentation/ml/overview.md:
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@@ -54,7 +54,7 @@ The recommended way to implement inference is by using the [RunInference API](ht
 
 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).
+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. 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:
   Where? Can you please point me towards it as I am unable to find it



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