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 Duration 7 hours

Course Outline

Introduction to Lightweight LLMs

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

Exploring Nano Banana

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

Deployment Strategies and Scenarios

  • Advantages of on-device execution
  • Comparing local versus cloud inference
  • Choosing the optimal deployment approach

Real-World Applications Across Sectors

  • Streamlining internal automation and knowledge support
  • Implementing customer-facing solutions
  • Addressing operational and compliance-driven needs

Integration Essentials

  • Reviewing system requirements
  • Considering workflow and process impacts
  • Overview of APIs and toolchains

Cost Management and Efficiency

  • Lowering inference expenses with compact models
  • Optimizing the balance between performance and resources
  • Strategizing for scalable implementations

Governance, Privacy, and Risk Oversight

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

Readiness for Organizational Integration

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

Summary and Future Directions

Requirements

  • Foundational knowledge of general IT principles
  • Proficiency with basic software tools
  • Experience with data-driven business processes

Intended Audience

  • General IT teams integrating AI capabilities
  • Business users exploring practical AI solutions
  • Technology leaders assessing on-device LLM strategies

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