Fraud detection on GCP
A common use case in machine learning, this tutorial is an end-to-end, production-ready fraud prediction system. It predicts in real-time whether a transaction made by a user is fraudulent.
Throughout this tutorial, we’ll walk through the creation of a production-ready fraud prediction system. A prediction is made in real-time as the user makes the transaction, so we need to be able to generate a prediction at low latency.
Our end-to-end example will perform the following workflows:
Computing and backfilling feature data from raw data
Building point-in-time correct training datasets from feature data and training a model
Making online predictions from feature data
Here's a high-level picture of our system architecture on Google Cloud Platform (GCP):
![](https://docs.feast.dev/~gitbook/image?url=https%3A%2F%2F3817298158-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252Fgy9PuNI7grENagH3bxBf%252Fuploads%252Fgit-blob-f5a0dd450b6e71634d960ce2af36d14f435ff8ef%252Fdata-systems-fraud-2x.jpg%3Falt%3Dmedia&width=768&dpr=4&quality=100&sign=97974484&sv=1)
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