Introduction
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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:
Visualize data with Data Refinery
Create and deploy a machine learning model
Monitor the model
Create a Python app to use the model
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.
Topic
Content
Introduction
Introduction Video
Platform Overview
CP4DaaS Overview
[Lab] Project Setup video | Instructions
Data Wrangling
Data Wrangling Overview
[Lab] Data Refinery video | Instructions
Data Management
Watson Knowledge Catalog Overview
[Demo] WKC
Machine Learning
Machine Learning into
[Lab] ML with Jupyter Notebook video | Instructions
[Lab] Automated ML with AutoAI Lab video | Instructions
Model Deployment
Model Deployment intro
[Lab] Online Deployment & testing video | Instructions
[Lab] Batch Scoring video | Instructions
[Lab] Deploy model to Python app video | Instructions
Model Monitoring
Monitoring & Explainability intro
[Demo] OpenScale
Conclusion
Conclusion Video
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
Cloud Pak for Data as a Service provides you with an integrated set of capabilities for collecting and organizing your data into a trusted, unified view, and then creating and scaling AI models across your business.
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 directory of the GitHub repository you will download in the pre-work section.
