Introduction
WARNING: This repository is no longer maintained :warning:
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.
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](https://archive.ics.uci.edu/ml/datasets/Statlog+(German+Credit+Data)). 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 | 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 | 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 | Use and Enterprise data catalog to search, manage, and protect data | |
00:05 | Use and Enterprise data catalog to search, manage, and protect data | |
00:20 | Create new Categories, Business terms, Policies and Rules in Watson Knowledge Catalog | |
00:05 | 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 | 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 | 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 | Quickly deploy an OpenScale demo with Auto setup | |
00:10 | Quickly deploy an OpenScale demo with Auto setup | |
00:30 | 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
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