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• 1. Background

  • 1.1 Setting the stage

  • 1.2 Current Process

  • 1.3 Proposed Improvements

  • 1.4 Examples from prototype

  • 1.5 Results and Next Steps

• 2. Connection and Setup ({user})

  • 2.1 Getting connected

  • 2.2 Pre-Created project and pipeline server

  • 2.3 Pre-Created workbench

  • 2.4 Your first Jupyter Notebook

  • 2.5 Validating the environment

• 3. Working with an LLM

  • 3.1 Notebook-Based LLM Example

  • 3.2 Text Summarization

  • 3.3 Information Extraction

  • 3.4 Comparing Model Servers

  • 3.5 Retrieval-Augmented Generation

  • 3.6 Confidence-check pipeline

  • 3.7 Prompt Engineering Exercise (Optional)

• 4. Image Processing

  • 4.1 Overall Approach

  • 4.2 Car recognition (Optional)

  • 4.3 Model retraining (Optional)

  • 4.4 Accident/Damage recognition (Optional)

  • 4.5 Model Serving

• 5. Web App Deployment

  • 5.1 Application overview

  • 5.2 OpenShift Terminal

  • 5.3 Deploying the application via GitOps

  • 5.4 Validating the application

  • 5.5 Process claims with a pipeline

• 6. Productization and Extrapolations

  • 6.1 Potential improvements and refinements

  • 6.2 Applicability to other industries

• 7. End of Lab

  • 7.1 Thanks