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Course Outline

Introduction to Lightweight LLMs

  • Exploring compact model architectures
  • The development of resource-efficient AI
  • The importance of lightweight models for enterprises

Understanding Nano Banana

  • Core features and design philosophy
  • Model capabilities and constraints
  • How Nano Banana compares to traditional LLMs

Deployment Models and Use Scenarios

  • Benefits of on-device execution
  • Local versus cloud-based inference
  • Choosing the appropriate deployment strategy

Practical Applications Across Industries

  • Internal automation and knowledge support
  • Customer-facing applications
  • Operational and compliance-related scenarios

Integration Fundamentals

  • Evaluating system requirements
  • Workflow and process considerations
  • Introduction to APIs and toolchains

Cost Optimization and Efficiency

  • Lowering inference costs with compact models
  • Striking a balance between performance and resources
  • Planning for scalable deployments

Governance, Privacy, and Risk Management

  • Ensuring secure on-device operation
  • Understanding data boundaries and protections
  • Alignment with enterprise policies and standards

Preparing for Organizational Adoption

  • Building internal capacity and readiness
  • Assessing business value through pilot projects
  • Setting the stage for wider implementation

Summary and Next Steps

Requirements

  • Foundational knowledge of general IT principles
  • Experience using basic software utilities
  • Familiarity with data-driven business processes

Target Audience

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  • IT teams looking to adopt AI solutions
  • Business professionals interested in practical AI tools
  • Tech managers evaluating on-device LLM strategies
 7 Hours

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