• Home
  • 1. Getting Started
    • Self-paced Workshop
      • A. Create an AWS account
      • B. Obtain an IBM Cloud Pak for Data trial license
      • C. Red Hat OpenShift Container Platform Trial
      • D. Service Instances Provisioning
      • E. Infrastructure Provisioning & Setup
    • AWS Event
      • AWS Workshop Portal
  • 2. Infra Provisioning Lab
    • Start Here
  • 3. Trusted AI Lab
    • Overview
    • Introduction
      • A. Architecture Diagram
      • B. Featured Technologies
      • C. Included Components
      • D. IBM-AWS Better Together
      • B. Know more about the Notebooks
      • E. Watch the Video
    • Pre-requisites
    • Start the lab
      • 1. Build Predictive models in SageMaker
      • 2. Create projects in Watson Studio
      • 3. Upload the SageMaker notebooks into Watson Studio project
      • 4. Run the notebooks in Watson Studio to generate predictions and endpoints
      • 5. Setup Watson Open Scale for monitoring SageMaker endpoints
      • 6. Monitor SageMaker endpoints using Watson Open Scale
      • 7. Visualization
        • 7.1. Setup Data Source & Cognos Embedded Dashboard
        • 7.2. Insights from Cognos Embedded Dashboard
    • Summary
    • License
  • 4. Governance Lab
    • Introduction
      • A. Reference Architecture
      • B. Use Cases
      • C. Featured Technologies
      • D. Included Components
      • E. IBM-AWS Better Together
      • F. Watch the Video
    • Start the lab
      • 1. Data Virtualization Lab
      • 2. Data Integration (ETL) Lab
      • 3. Data Cleansing & Reshaping Lab
      • 4. Data Governance Lab
    • Summary
  • 5. Visualization & Insights Lab
    • Introduction
      • A. Reference Architecture
      • B. Use Cases
      • C. Featured Technologies
      • D. Included Components
      • E. IBM-AWS Better Together
      • F. Watch the Video
    • Start the lab
      • 1. Launch IBM Cognos Analytics
      • 2. Connect to AWS Aurora Postgres DB
      • 3. Connect to Data virtualization data source
      • 4. Create a data module for Risk index and PII dashboard
      • 5. Create a data module for Data virtualization source
      • 6. Create and visualize IBM Cognos Analytics dashboard
      • 7. Showcasing the AI capabilities of Cognos Analytics
  • 6. Low/No Code ML Lab
    • Introduction
      • A. Reference Architecture
      • B. Use Cases
      • C. Featured Technologies
      • D. Included Components
      • E. IBM-AWS Better Together
    • Start the lab
      • 1. Setup a project in Cloud Pak for Data
      • 2. Code Approach
        • 2.1. Run LSTM Notebook 1
        • 2.2. Run LSTM Notebook 2
        • 2.3. Run Decision Tree Notebook
      • 3. Visualization
        • 3.1. Setup Cognos Embedded Dashboard
        • 3.2. Analyze Cognos Embedded Dashboard
      • 4. No Code Approach
        • 4.1. Build and Deploy model with AutoAI
        • 4.2. Configure SAM
        • 4.3. RI-Prediction App
    • Summary
  • 7. Conclusion
    • Cleanup

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IBM Cloud Pak for Data (CP4D) on AWS Modernization Workshop > Visualization & Insights Lab > Start the lab

Start the lab

Steps:

  • 1. Launch IBM Cognos Analytics
  • 2. Connect to AWS Aurora Postgres DB
  • 3. Connect to Data virtualization data source
  • 4. Create a data module for Risk index and PII dashboard
  • 5. Create a data module for Data virtualization source
  • 6. Create and visualize IBM Cognos Analytics dashboard
  • 7. Showcasing the AI capabilities of Cognos Analytics