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Course Outline

Introduction to Lightweight LLMs

  • Examining compact model architectures
  • The progression of resource-efficient AI
  • The importance of lightweight models for enterprise environments

Understanding Nano Banana

  • Core features and underlying design principles
  • Exploring model strengths and limitations
  • Distinguishing Nano Banana from conventional LLMs

Deployment Models and Use Scenarios

  • Benefits of on-device execution
  • Comparing local versus cloud-based inference
  • Determining the optimal deployment strategy

Practical Applications Across Industries

  • Internal automation and knowledge support
  • Customer-interfacing use cases
  • Operational and compliance-focused scenarios

Integration Fundamentals

  • Reviewing system requirements
  • Considerations for workflow and process integration
  • Introduction to APIs and the toolchain

Cost Optimisation and Efficiency

  • Lowering inference costs through compact models
  • Balancing performance with resource usage
  • Strategising for scalable deployments

Governance, Privacy, and Risk Management

  • Safeguarding secure on-device execution
  • Understanding data boundaries and protective measures
  • Aligning with enterprise policies and standards

Preparing for Organizational Adoption

  • Developing internal skills and readiness
  • Evaluating business value via pilot projects
  • Establishing the foundation for wider rollouts

Summary and Next Steps

Requirements

  • A solid grasp of general IT fundamentals
  • Proficiency with standard software tools
  • Knowledge of data-centric business processes

Target Audience

  • General IT teams looking to adopt AI capabilities
  • Business professionals interested in real-world AI applications
  • Technology leaders evaluating strategies for on-device LLMs
 7 Hours

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