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Course Outline

Introduction to Lightweight LLMs

  • Exploring compact model architectures
  • The trajectory of resource-efficient AI development
  • The significance of lightweight models in enterprise settings

Decoding Nano Banana

  • Core features and design philosophy
  • Model strengths and inherent constraints
  • Distinguishing Nano Banana from conventional LLMs

Deployment Strategies and Application Scenarios

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

Cross-Industry Practical Applications

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

Integration Essentials

  • Assessing system prerequisites
  • Considerations for workflow and process alignment
  • Overview of APIs and toolchains

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 Oversight

  • Safeguarding secure on-device operations
  • Navigating data boundaries and protective measures
  • Aligning with corporate policies and industry standards

Readying for Organizational Integration

  • Developing internal competencies and readiness
  • Evaluating business impact via pilot initiatives
  • Establishing the foundation for broader deployment

Conclusion and Future Directions

Requirements

  • A solid grasp of fundamental IT concepts
  • Proficiency with essential software tools
  • Knowledge of data-centric business processes

Target Audience

  • IT teams integrating AI capabilities into their operations
  • Business stakeholders interested in practical AI use cases
  • Technology leaders assessing on-device LLM strategies
 7 Hours

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