Introduction

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](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.

This file has the following attributes:

  • CUSTOMERID (hex number, used as Primary Key)

  • CHECKINGSTATUS

  • CREDITHISTORY

  • EXISTINGSAVINGS

  • INSTALLMENTPLANS

  • EXISTINGCREDITSCOUNT

This file has the following attributes:

  • CUSTOMERID

  • LOANDURATION

  • LOANPURPOSE

  • LOANAMOUNT

  • INSTALLMENTPERCENT

  • OTHERSONLOAN

  • RISK

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