willb / fraud-notebooks

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fraud-notebooks

These notebooks accompany several talks and workshops developed by Will Benton and Sophie Watson.

  • Our workshop "From Statistics to Serverless: Intelligent Applications on OpenShift 4" was delivered at Red Hat Summit and IBM Think in 2020. Slides are available as a PDF or as a movie. The rest of the instructions in this README will cover getting the basic application running.
  • Our GTC 2021 talk "Fighting Fraud With One App In Many Ways: GPU-Accelerated End MLOps on Kubernetes" built up a similar fraud-detection application in two versions using RAPIDS.ai. Notebooks from that talk are on this branch.

In order to build and run a model service, you'll need an OpenShift cluster, but you can experiment with the notebooks on your own time. Here's how:

The easy way

Use binder. (We don't recommend this if you'll be running the tutorial over conference wifi, but it requires almost no setup and can run from a computer that only has a browser.)

The flexible way

If you want to experiment with the data generator, you'll want to use your own computer.

Install the prerequisites

  1. Make sure you have Python 3.7 installed, installing it if necessary
    • If you have a favorite package manager, use that
    • if not, python.org has binaries for many platforms
  2. Make sure you have git installed, installing it if necessary
    • If you have a favorite package manager, use that
    • if not, git-scm.com has binaries for many platforms (you won't need a GUI)
  3. Install pipenv
    • on a Mac, the easiest way is probably brew install pipenv
    • on a Fedora Linux machine, the easiest way is probably dnf install pipenv
    • on Windows, if you have Python installed already, the easiest way is probably to use pip

Install the notebooks and dependencies

  1. Clone this repository: git clone https://github.com/willb/fraud-notebooks/
  2. Change to this repository's directory: cd fraud-notebooks
  3. Install the dependencies: pipenv install
  4. Run the notebooks: pipenv run jupyter notebook

Binder

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