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):

A high-level architecture of system using Feast for fraudulent transactions
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