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 Duration 7 hours

Course Outline

Introduction to Lightweight LLMs

  • Examining compact model architectures
  • Tracing the development of resource-efficient AI
  • The strategic importance of lightweight models for enterprises

Delving into Nano Banana

  • Core features and architectural design principles
  • Assessing model capabilities and constraints
  • Distinguishing Nano Banana from conventional LLMs

Deployment Models and Application Scenarios

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

Cross-Industry Practical Applications

  • Enhancing internal automation and knowledge support
  • Implementing customer-facing solutions
  • Addressing operational and compliance requirements

Integration Essentials

  • Reviewing system requirements
  • Considering workflow and process impacts
  • Overview of API and toolchain integration

Cost Efficiency and Optimization

  • Lowering inference expenses through compact models
  • Striking a balance between performance and resource usage
  • Planning for scalable implementation

Governance, Privacy, and Risk Mitigation

  • Securing on-device execution environments
  • Navigating data boundaries and protective measures
  • Aligning with enterprise policies and industry standards

Preparing for Enterprise Adoption

  • Developing internal competence and readiness
  • Measuring business impact via pilot projects
  • Establishing the foundation for wider deployment

Key Takeaways and Future Directions

Requirements

  • A solid grasp of general IT fundamentals
  • Proficiency with basic software applications
  • Acquaintance with data-centric business processes

Target Audience

  • IT teams integrating AI capabilities into their infrastructure
  • Business professionals seeking practical AI applications
  • Technology leaders assessing on-device LLM strategies

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