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WARNING: This repository is no longer maintained ⚠️

This repository does not have active maintainers. Pull requests for fixes and enhancements will still be accepted, but no active work will be done on this workshop.

This Workshop uses Cloud Pak for Data version 3.5

Analyzing Credit Risk with Cloud Pak for Data on OpenShift

Welcome to our workshop! In this workshop we'll be using the Cloud Pak for Data platform to Collect Data, Organize Data, Analyze Data, and Infuse AI into our applications. The goals of this workshop are:

  • Collect and virtualize data
  • Visualize data with Data Refinery
  • Create and deploy a machine learning model
  • Monitor the model
  • Create a Python app to use the model

About this workshop

About the data set

In this workshop we will be using a credit risk / lending scenario. In this scenario, lenders respond to an increased pressure to expand lending to larger and more diverse audiences, by using different approaches to risk modeling. This means going beyond traditional credit data sources to alternative credit sources (i.e. mobile phone plan payment histories, education, etc), which may introduce risk of bias or other unexpected correlations.

Use Case Diagram

The credit risk model that we are exploring in this workshop uses a training data set that contains 20 attributes about each loan applicant. The scenario and model use synthetic data based on the UCI German Credit dataset. The data is split into three CSV files and are located in the data directory of the GitHub repository you will download in the pre-work section.

Applicant Financial Data

This file has the following attributes:

  • CUSTOMERID (hex number, used as Primary Key)
  • CHECKINGSTATUS
  • CREDITHISTORY
  • EXISTINGSAVINGS
  • INSTALLMENTPLANS
  • EXISTINGCREDITSCOUNT

Applicant Loan Data

This file has the following attributes:

  • CUSTOMERID
  • LOANDURATION
  • LOANPURPOSE
  • LOANAMOUNT
  • INSTALLMENTPERCENT
  • OTHERSONLOAN
  • RISK

Applicant Personal Data

This file has the following attributes:

  • CUSTOMERID
  • EMPLOYMENTDURATION
  • SEX
  • CURRENTRESIDENCEDURATION
  • OWNSPROPERTY
  • AGE
  • HOUSING
  • JOB
  • DEPENDENTS
  • TELEPHONE
  • FOREIGNWORKER
  • FIRSTNAME
  • LASTNAME
  • EMAIL
  • STREETADDRESS
  • CITY
  • STATE
  • POSTALCODE

Agenda
00:05 Welcome Welcome to the Cloud Pak for Data workshop
00:20 Lecture - Intro and Overview Introduction to Cloud Pak for Data and an Overview of this workshop
00:20 Lab - Pre-work Clone or Download the repo, create a project, create a deployment space
00:10 Walkthrough - Pre-work Clone or Download the repo, create a project, create a deployment space
00:20 Lecture - Data Refinery and Data Virtualization Data Refinery and Data Virtualization
00:30 Lab - Data Connection and Virtualization and importing the data into the project Creating a new connection, virtualizing the data, importing the data into the project
00:10 Walkthrough - Data Connection and Virtualization Creating a new connection, virtualizing the data, importing the data into the project
00:15 Lab - Import Data into Project Importing data in your projects
00:05 Walkthrough - Import Data into Project Importing data in your projects
00:15 Lab - Data Visualization with Data Refinery Refining the data, visualizing and profiling the data
00:10 Walkthrough - Data Visualization with Data Refinery Refining the data, visualizing and profiling the data
00:15 Lecture - Watson Knowledge Catalog Enterprise governance with Watson Knowledge Catalog
00:20 Lab - Enterprise data governance for Viewers using Watson Knowledge Catalog Use and Enterprise data catalog to search, manage, and protect data
00:05 Walkthrough - Enterprise data governance for Viewers using Watson Knowledge Catalog Use and Enterprise data catalog to search, manage, and protect data
00:20 Lab - Enterprise data governance for Admins using Watson Knowledge Catalog Create new Categories, Business terms, Policies and Rules in Watson Knowledge Catalog
00:05 Walkthrough - Enterprise data governance for Admins using Watson Knowledge Catalog Create new Categories, Business terms, Policies and Rules in Watson Knowledge Catalog
00:15 Lecture - Machine Learning Machine Learning and Deep Learning concepts
00:20 Lab - Machine Learning with Jupyter Building a model with Spark, deploying the model with Watson Machine Learning, testing the model with a Python Flask app
00:10 Walkthrough - Machine Learning with Jupyter Building a model with Spark, deploying the model with Watson Machine Learning, testing the model with a Python Flask app
00:20 Lab - AutoAI - Machine Learning with AutoAI Use AutoAi to quickly generate a Machine Learning pipeline and model
00:10 Walkthrough - Machine Learning with AutoAI Use AutoAi to quickly generate a Machine Learning pipeline and model
00:30 Lab - Deploy and Test Machine Learning Models Deploy and machine learning models using several approaches
00:10 Walkthrough - Deploy and Test Machine Learning Models Deploy and machine learning models using several approaches
00:15 Lab - Monitoring models with OpenScale GUI (Auto setup Monitoring) Quickly deploy an OpenScale demo with Auto setup
00:10 Walkthrough - Monitoring models with OpenScale GUI (Auto setup Monitoring) Quickly deploy an OpenScale demo with Auto setup
00:30 Lab - Monitoring models with OpenScale (Notebook) See the OpenScale APIs in a Jupyter notebook and manually configure the monitors
00:10 Walkthrough - Monitoring models with OpenScale (Notebook) See the OpenScale APIs in a Jupyter notebook and manually configure the monitors
00:10 Closing Other capabilities, review, and next steps

Compatability

This workshop has been tested on the following platforms:

  • macOS: Mojave (10.14), Catalina (10.15)

    • Google Chrome version 81

  • Microsoft: Windows 10

    • Google Chrome, Microsoft Edge