Demo Platform AI/ML Lifecycle Automation at the Edge
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Red Hat

AI/ML Lifecycle Automation at the Edge

    • 1. AI & Edge Use case
      • 1.1 Understanding our case
      • 1.2 Essentials of Edge
      • 1.3 Explore our design
    • 2. Dashboards Control Center
      • 2.1 Control center
    • 3. Autonomous Vehicle
      • 3.1 Vehicle infra
      • 3.2 Deploying MinIO
      • 3.3 Serving models
      • 3.4 Deploying the BMS app
    • 4. RHOAI configuration
      • 4.1 RHOAI installation
      • 4.2 Datascience projects
      • 4.3 DataConnections
    • 5. Model Re-training
      • 5.1 Workbenches
      • 5.2 Import Notebooks
      • 5.3 Training our models
    • 6. Model Serving
      • 6.1 Stress Detection serving
      • 6.2 Time to Failure serving
      • 6.3 Querying endpoints
    • 7. DataScience Pipelines
      • 7.1 Pipeline server
      • 7.2 Pipeline execution
      • 7.3 Pipeline automation
    • 8. Model Monitoring
      • 8.1 TrustyAI
      • 8.2 Detect bias
      • 8.3 Data drift
    • 9. Battery Monitoring System
      • 9.1 Testing alerts
  • AI/ML Lifecycle Automation at the Edge
    • master
  • AI/ML Lifecycle Automation at the Edge
  • 9. Battery Monitoring System
  • 9.1 Testing alerts
8.3 Data drift

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