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Adding official version for Fraud detection notebook #321
Adding official version for Fraud detection notebook #321
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Check out this pull request on See visual diffs & provide feedback on Jupyter Notebooks. Powered by ReviewNB |
/gcbrun |
/gcbrun |
tldr: pip install joblib. You don't need an IS_TESTING check as this is a required dependency. Don't worry about what Workbench or Colab have preinstalled, just install any dependencies that our base image doesn't provide. That is the source-of-truth. |
…an-SpringML/vertex-ai-samples into fraud_detection_notebook
notebook ready for review |
/gcbrun |
@@ -9,6 +9,57 @@ | |||
"# Build a fraud detection model on Vertex AI" |
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Line #8. serving_container_image_uri="us-docker.pkg.dev/vertex-ai/prediction/sklearn-cpu.0-24:latest",
Can we pull this out as a constant variable? Also, let's use the latest version 1.0 listed here: https://cloud.google.com/vertex-ai/docs/predictions/pre-built-containers
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@sudarshan-SpringML added one comment here: https://app.reviewnb.com/GoogleCloudPlatform/vertex-ai-samples/pull/321/ Other than that, looks good! |
/gcbrun |
@kweinmeister I think this cell is failing for me-> "forest = RandomForestClassifier() |
added debug statements to find the cell where kernel dies |
/gcbrun |
/gcbrun |
Weird error. See if I can force a retest. Traceback (most recent call last): |
/gcbrun |
Step #3: Traceback (most recent call last): |
@kweinmeister Has anyone debugged the reason for kernel died? |
/gcbrun |
kernel died again |
/gcbrun |
Seems like it's dying on Try initializing RandomForestClassifier with Reference: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html |
Done |
@sudarshan-SpringML Please run this notebook manually in Workbench AI and see if the kernel dies or not. |
Hi @andrew Ferlitsch ***@***.***>. I ran notebook in workbench
yesterday. It worked fine
…On Thu, Apr 21, 2022, 7:54 AM Andrew Ferlitsch ***@***.***> wrote:
@sudarshan-SpringML <https://github.com/sudarshan-SpringML> Please run
this notebook manually in Workbench AI and see if the kernel dies or not.
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per comment, notebook runs a-ok in Workbench, so will manually merge. |
This PR contains a notebook which shows you how to build, deploy, and analyze predictions from a simple random forest model using tools like scikit-learn, Vertex AI, and the What-IF Tool (WIT) on a synthetic fraud transaction dataset to solve a financial fraud detection problem.
# Official Notebooks
section, pointing to the author or the author's team.