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Course Outline

Introduction to Lightweight LLMs

  • Grasping compact model architectures
  • The progression of resource-efficient AI
  • The importance of lightweight models for enterprises

Understanding Nano Banana

  • Core features and design philosophy
  • Model capabilities and constraints
  • Distinguishing Nano Banana from conventional LLMs

Deployment Models and Use Scenarios

  • Benefits of on-device execution
  • Comparing local and 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

  • Assessing system requirements
  • Considering workflow and process adjustments
  • Overview of APIs and toolchains

Cost Optimization and Efficiency

  • Lowering inference costs with compact models
  • Optimizing the balance between performance and resources
  • Planning for scalable deployment

Governance, Privacy, and Risk Management

  • Ensuring secure execution on devices
  • Understanding data boundaries and protections
  • Aligning with enterprise policies and standards

Preparing for Organizational Adoption

  • Developing internal expertise and readiness
  • Evaluating business value via pilot projects
  • Establishing the foundation for wider implementation

Summary and Next Steps

Requirements

  • Familiarity with core IT concepts
  • Experience using basic software tools
  • Knowledge of data-driven business processes

Target Audience

  • IT teams integrating AI capabilities
  • Business professionals interested in practical AI solutions
  • Technology leaders evaluating on-device LLM strategies
 7 Hours

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