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Add example of real time Anomaly Detection using RunInference #23497
Add example of real time Anomaly Detection using RunInference #23497
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@damccorm @andyxiexu please have a look and give your comments. |
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- Coverage 73.45% 73.33% -0.12%
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Lines 95884 95798 -86
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- Hits 70427 70249 -178
- Misses 24146 24238 +92
Partials 1311 1311
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Overall looks good to me, had a number of smaller comments (mostly wording)
website/www/site/content/en/documentation/ml/anomaly-detection.md
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...che_beam/examples/inference/anomaly_detection/anomaly_detection_pipeline/pipeline/options.py
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Just a few lingering comments, should be good to go after those though. Thanks!
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sdks/python/apache_beam/examples/inference/anomaly_detection/anomaly_detection_pipeline/main.py
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...ython/apache_beam/examples/inference/anomaly_detection/write_data_to_pubsub_pipeline/main.py
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@@ -59,3 +59,4 @@ In order to automate and track the AI/ML workflows throughout your project, you | |||
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You can find examples of end-to-end AI/ML pipelines for several use cases: |
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Please add more descriptions to this list, for each item a small paragraph of what this does with high level example of the use case shown .
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@Juta please have a look and let's discuss this
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I agree it would be good to add a little bit of detail on these, we can also probably defer that to a future PR though where we clean up that page more generally. @shub-kris @Juta thoughts?
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SGTM, I'll go ahead and merge and we can circle back to this
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Yes ok let's pick this up in a next PR
.../examples/inference/anomaly_detection/anomaly_detection_pipeline/pipeline/transformations.py
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retest this please |
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@@ -59,3 +59,4 @@ In order to automate and track the AI/ML workflows throughout your project, you | |||
|
|||
You can find examples of end-to-end AI/ML pipelines for several use cases: |
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I agree it would be good to add a little bit of detail on these, we can also probably defer that to a future PR though where we clean up that page more generally. @shub-kris @Juta thoughts?
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added one small comment, and it looks like there are 2 other lingering comments from Reza (the PROJECT_ID one and the overview.md one)
Thanks! I'll merge once tests complete |
This PR aims to illustrates an example of setting up a real time text anomaly detection pipeline using PubSub and RunInference.
The entire implementation is divided into two different pipelines:
write_data_to_pubsub_pipeline
pushes data to PubSubanomaly_detection_pipeline
reads data from PubSub, uses a trained HDBSCAN model and does anomaly detection.Thank you for your contribution! Follow this checklist to help us incorporate your contribution quickly and easily:
R: @username
).addresses #123
), if applicable. This will automatically add a link to the pull request in the issue. If you would like the issue to automatically close on merging the pull request, commentfixes #<ISSUE NUMBER>
instead.CHANGES.md
with noteworthy changes.See the Contributor Guide for more tips on how to make review process smoother.
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