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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):
 Run in Google Colab |
 View Source on Github |
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