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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

Exploring Nano Banana

  • Core features and design philosophy
  • Model capabilities and inherent limitations
  • How Nano Banana contrasts with traditional LLMs

Deployment Models and Application Scenarios

  • Benefits of on-device execution
  • Comparing local and cloud inference
  • Choosing the optimal deployment approach

Practical Applications Across Industries

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

Integration Fundamentals

  • Assessing system requirements
  • Considering workflow and process impacts
  • Introduction to APIs and toolchains

Cost Optimization and Efficiency

  • Lowering inference costs with compact models
  • Striking a balance between performance and resource usage
  • Strategizing for scalable deployments

Governance, Privacy, and Risk Management

  • Ensuring secure on-device execution
  • Navigating data boundaries and security measures
  • Aligning with enterprise policies and standards

Preparing for Organizational Adoption

  • Developing internal capacity and readiness
  • Evaluating business value through pilot initiatives
  • Establishing the foundation for wider implementation

Summary and Next Steps

Requirements

  • General knowledge of IT concepts
  • Proficiency with standard software tools
  • Understanding of data-centric business workflows

Target Audience

  • IT teams implementing AI capabilities
  • Business professionals seeking practical AI solutions
  • Technology leaders assessing on-device LLM strategies
 7 Hours

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