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Course Outline

Introduction to Compact LLMs

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

Understanding Nano Banana

  • Core features and design principles
  • Model capabilities and limitations
  • How Nano Banana differs from traditional LLMs

Deployment Models and Use Scenarios

  • On-device execution and its advantages
  • Local versus cloud inference
  • Selecting the appropriate deployment path

Practical Applications Across Industries

  • Internal automation and knowledge assistance
  • Customer-facing use cases
  • Operational and compliance-driven scenarios

Integration Fundamentals

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

Cost Optimization and Efficiency

  • Reducing inference costs through compact models
  • Balancing performance and resources
  • Planning for scalable deployments

Governance, Privacy, and Risk Management

  • Ensuring secure on-device execution
  • Understanding data boundaries and safeguards
  • Alignment with enterprise policies and standards

Preparing for Organizational Adoption

  • Building internal capability and readiness
  • Assessing business value through pilot projects
  • Establishing the foundation for broader rollouts

Summary and Next Steps

Requirements

  • Familiarity with general IT concepts
  • Experience with basic software tools
  • Understanding of data-driven business workflows

Audience

  • IT teams adopting AI capabilities
  • Business users seeking practical AI applications
  • Tech managers evaluating on-device LLM strategies
 7 Hours

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