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Course Outline

Introduction to Compact LLMs

  • Grasping 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 capabilities and constraints
  • How Nano Banana stands apart from traditional LLMs

Deployment Models and Use Cases

  • On-device execution and its advantages
  • Local versus cloud inference
  • Choosing the appropriate deployment strategy

Practical Applications Across Sectors

  • Internal automation and knowledge support
  • Customer-facing implementations
  • Operational and compliance-driven scenarios

Integration Essentials

  • Assessing system needs
  • Considering workflow and process factors
  • Overview of APIs and toolchains

Cost Optimization and Efficiency

  • Lowering inference costs through 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 protective measures
  • Alignment with enterprise policies and standards

Preparing for Organizational Adoption

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

Summary and Next Steps

Requirements

  • Basic knowledge of IT concepts
  • Experience using fundamental software tools
  • Familiarity with data-centric business processes

Target Audience

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

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