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https://issues.apache.org/jira/browse/BEAM-9421?focusedWorklogId=404344&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-404344
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ASF GitHub Bot logged work on BEAM-9421:
----------------------------------------
Author: ASF GitHub Bot
Created on: 17/Mar/20 00:12
Start Date: 17/Mar/20 00:12
Worklog Time Spent: 10m
Work Description: limingxi commented on pull request #11075: [BEAM-9421]
Website section that describes getting predictions using AI Platform Prediciton
URL: https://github.com/apache/beam/pull/11075#discussion_r393378386
##########
File path: website/src/documentation/patterns/ai-platform.md
##########
@@ -0,0 +1,87 @@
+---
+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 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 a cloud-hosted machine learning model to make
predictions about new data using Google Cloud AI Platform Prediction within
Beam's pipeline.
+
+[tfx_bsl](https://github.com/tensorflow/tfx-bsl) is a library that provides
`RunInference` Beam's PTransform. `RunInference` is a PTransform able to
perform two types of inference. One of them can use a service endpoint. When
using a service endpoint, the transform takes a PCollection of type
`tf.train.Example` and, for each element, sends a request to Google Cloud AI
Platform Prediction service. The transform produces a PCollection of type
`PredictLog` which contains predictions.
+
+Before getting started, deploy a machine learning model to the cloud. The
cloud service manages the infrastructure needed to handle prediction requests
in both efficient and scalable way. Only Tensorflow models are supported. For
more information, see [Exporting a SavedModel for
prediction](https://cloud.google.com/ai-platform/prediction/docs/exporting-savedmodel-for-prediction).
+
+Once a machine learning model is deployed, prepare a list of instances to get
predictions for.
+
+Here is an example of a pipeline that reads input instances from the file,
converts JSON objects to `tf.train.Example` objects and sends data to the
service. The content of a file can look like this:
+
+```
+{"input": "the quick brown"}
+{"input": "la bruja le"}
+```
+
+The example creates `tf.train.BytesList` instances, thus it expects byte-like
strings as input, but other data types, like `tf.train.FloatList` and
`tf.train.Int64List`, are also supported by the transform. To send binary data,
make sure that the name of an input ends in `_bytes`.
Review comment:
I think it would be good to list all possible input formats and output
formats here or somewhere else for reference. And for the last sentence, do you
mean that we need to change l74 to something like:
feature={name+'_bytes', value} for sending binary data to endpoint?
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Issue Time Tracking
-------------------
Worklog Id: (was: 404344)
Time Spent: 3h 50m (was: 3h 40m)
> AI Platform pipeline patterns
> -----------------------------
>
> Key: BEAM-9421
> URL: https://issues.apache.org/jira/browse/BEAM-9421
> Project: Beam
> Issue Type: Sub-task
> Components: website
> Reporter: Kamil Wasilewski
> Assignee: Kamil Wasilewski
> Priority: Major
> Labels: pipeline-patterns
> Time Spent: 3h 50m
> Remaining Estimate: 0h
>
> New pipeline patterns should be contributed to the Beam's website in order to
> demonstrate how newly implemented Google Cloud AI PTransforms can be used in
> pipelines.
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