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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 in enterprise environments

Understanding Nano Banana

  • Core features and foundational design principles
  • Defining model capabilities and inherent limitations
  • Distinguishing Nano Banana from conventional LLMs

Deployment Models and Use Scenarios

  • Benefits of on-device execution
  • Comparing local versus cloud-based inference
  • Choosing the optimal deployment strategy

Practical Applications Across Industries

  • Enhancing internal automation and knowledge support
  • Implementing customer-facing AI solutions
  • Addressing operational and compliance-related needs

Integration Fundamentals

  • Assessing system requirements
  • Considerations for workflow and process optimization
  • Introduction to APIs and toolchains

Cost Optimization and Efficiency

  • Leveraging compact models to lower inference costs
  • Striking a balance between performance and resource usage
  • Planning for scalable implementation

Governance, Privacy, and Risk Management

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

Preparing for Organizational Adoption

  • Developing internal skills and readiness
  • Evaluating business impact through pilot initiatives
  • Establishing the foundation for wider deployment

Summary and Next Steps

Requirements

  • A solid grasp of general IT concepts
  • Proficiency with fundamental software tools
  • Experience with data-centric business workflows

Target Audience

  • IT teams looking to integrate AI capabilities
  • Business professionals seeking practical AI applications
  • Technology leaders evaluating on-device LLM strategies

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