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Course Outline

Foundations of Lightweight LLMs

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

Exploring Nano Banana

  • Core features and architectural design principles
  • Model strengths and inherent limitations
  • Distinguishing Nano Banana from conventional LLMs

Deployment Strategies and Application Scenarios

  • On-device execution and its associated advantages
  • Comparing local versus cloud-based inference
  • Determining the most effective deployment pathway

Practical Applications Across Various Sectors

  • Internal automation and knowledge support systems
  • Customer-facing application examples
  • Operational and compliance-focused use cases

Integration Essentials

  • Assessing necessary system requirements
  • Considerations for workflow and process integration
  • Overview of API and toolchain components

Optimizing Costs and Efficiency

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

Governance, Privacy, and Risk Mitigation

  • Safeguarding secure on-device execution
  • Comprehending data boundaries and protective measures
  • Ensuring alignment with corporate policies and standards

Readiness for Organizational Implementation

  • Cultivating internal skills and preparedness
  • Measuring business value via pilot initiatives
  • Establishing the foundation for wider adoption

Recap and Future Directions

Requirements

  • A foundational understanding of general IT concepts
  • Hands-on experience with basic software tools
  • Acquaintance with data-driven business workflows

Target Audience

  • IT teams in the process of adopting AI capabilities
  • Business stakeholders interested in practical AI implementations
  • Technology leaders evaluating on-device LLM strategies
 7 Hours

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