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Course Outline

Introduction to Lightweight LLMs

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

Understanding Nano Banana

  • Core features and design philosophy
  • Model strengths and constraints
  • Differentiators of Nano Banana compared to conventional LLMs

Deployment Models and Use Scenarios

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

Practical Applications Across Industries

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

Integration Fundamentals

  • Reviewing system prerequisites
  • Considerations for workflows and processes
  • Overview of APIs and the toolchain

Cost Optimization and Efficiency

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

Governance, Privacy, and Risk Management

  • Safeguarding secure on-device operations
  • Grasping data boundaries and protective measures
  • Aligning with enterprise policies and standards

Preparing for Organizational Adoption

  • Developing internal competence and readiness
  • Measuring business value via pilot initiatives
  • Establishing the foundation for wider implementation

Summary and Next Steps

Requirements

  • A foundational grasp of general IT concepts
  • Proficiency with basic software utilities
  • Acquaintance with data-centric business processes

Target Audience

  • IT teams integrating AI capabilities into their workflows
  • Business professionals exploring practical AI solutions
  • Technology leaders assessing on-device LLM strategies
 7 Hours

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