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Take the data fabric tutorials on Cloud Pak for Data v4.5.3

By SHARYN RICHARD posted Wed October 19, 2022 12:07 PM

  
Take data fabric tutorials in Cloud Pak for Data v4.5.3 to experience one or more of the use cases that combine to demonstrate how you can implement a data fabric solution. The tutorials use the story of Golden Bank, a leading mortgage provider through their network of neighborhood branches, and focus on different roles, data engineer, data scientist, data analyst, and data steward.


MLOps and trustworthy AI

Scenario: Golden Bank wants to expand its business by offering low-rate mortgage renewals for online applications. Online applications expand the bank's customer reach and reduce the bank's application processing costs.

Tutorials in this use case:

Tutorial 1: Build and deploy a model
Train a model, promote it to a deployment space, and deploy the model.
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Tutorial 2: Test and validate the model
Evaluate a model for accuracy, fairness, and explainability.
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Multicloud data integration

Scenario: Based on a new regulation, Golden Bank cannot lend to underqualified loan applicants. The bank needs a data pipeline that delivers concise, pre-processed, and up-to-date data on all mortgage applicants, so that lenders can make decisions.

Tutorial in this use case:

Tutorial 1: Integrate data
Extract, filter, join, and transform your data.

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Customer 360

Scenario: Golden Bank wants to run a campaign to offer lower mortgage rates. The bank needs a consolidated 360 view of applicants to identify the highest value customers to target and help to determine the best rates to offer them.

Tutorials in this use case:

Tutorial 1: Configure a 360-degree view
Set up, map, and model your data to create a 360-degree view of your customers.

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Tutorial 2: Explore your customers
Explore the 360-degree view to identify the best customers for the marketing campaign offers.

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Data governance and privacy

Scenario: Golden Bank has several departments that need access to high-quality customer and mortgage data. The bank needs to create a business vocabulary to describe and manage data assets, and then make those assets available in a self-service catalog.

Tutorials in this use case:

Tutorial 1: Trust your data
Create trusted data assets by enriching your data and running data quality analysis.
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Tutorial 2: Protect your data
Control access to data across Cloud Pak for Data as a Service.

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Tutorial 3: Know your data
Evaluate, share, shape, and analyze data.

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