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 Duration 7 hours

Course Outline

Introduction to Lightweight LLMs

  • Exploring compact model architectures
  • The progression of resource-efficient AI
  • The significance of lightweight models for enterprises

Comprehending Nano Banana

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

Deployment Models and Application Scenarios

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

Practical Applications Across Sectors

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

Integration Fundamentals

  • Assessing system requirements
  • Workflow and procedural considerations
  • Introduction to APIs and the toolchain

Cost Optimization and Efficiency

  • Lowering inference costs through compact models
  • Striking a balance between performance and resource usage
  • Planning for scalable deployment strategies

Governance, Privacy, and Risk Mitigation

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

Preparing for Organizational Adoption

  • Developing internal capabilities and readiness
  • Evaluating business impact through pilot initiatives
  • Establishing a foundation for wider implementation

Summary and Future Directions

Requirements

  • A solid grasp of general IT fundamentals
  • Proficiency with basic software tools
  • Working knowledge of data-driven business processes

Target Audience

  • IT teams integrating AI capabilities into their operations
  • Business professionals interested in practical AI implementations
  • Technology managers assessing on-device LLM strategies

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