[GitHub] [beam] rose-rong-liu commented on a change in pull request #11075: [BEAM-9421] Website section that describes getting predictions using AI Platform Prediciton

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rose-rong-liu commented on a change in pull request #11075:
URL: https://github.com/apache/beam/pull/11075#discussion_r421169300



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File path: website/src/documentation/patterns/ai-platform.md
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@@ -0,0 +1,90 @@
+---
+layout: section
+title: "AI Platform integration patterns"
+section_menu: section-menu/documentation.html
+permalink: /documentation/patterns/ai-platform/
+---
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# AI Platform integration patterns
+
+This page describes common patterns in pipelines with Google Cloud AI Platform transforms.
+
+<nav class="language-switcher">
+  <strong>Adapt for:</strong>
+  <ul>
+    <li data-type="language-java">Java SDK</li>
+    <li data-type="language-py" class="active">Python SDK</li>
+  </ul>
+</nav>
+
+## Getting predictions
+
+This section shows how to use [Google Cloud AI Platform Prediction](https://cloud.google.com/ai-platform/prediction/docs/overview) to make predictions about new data from a cloud-hosted machine learning model.
+ 
+[tfx_bsl](https://github.com/tensorflow/tfx-bsl) is a library with a Beam PTransform called `RunInference`. `RunInference` is able to perform an inference that can use an external service endpoint for receiving data. When using a service endpoint, the transform takes a PCollection of type `tf.train.Example` and, for every batch of elements, sends a request to AI Platform Prediction. The size of a batch may vary. For more details on how Beam finds the best batch size, refer to a docstring for [BatchElements](https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.util.html?highlight=batchelements#apache_beam.transforms.util.BatchElements).
+ 
+ The transform produces a PCollection of type `PredictLog`, which contains predictions. 

Review comment:
       s/PredictLog/PredictionLog




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