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Course Outline

Introduction to Compact LLMs

  • Exploring efficient model architectures
  • The progress of resource-smart AI
  • Why small-scale models are valuable for enterprises

Understanding Nano Banana

  • Core features and design philosophy
  • Model strengths and boundaries
  • What sets Nano Banana apart from standard LLMs

Deployment Methods and Use Cases

  • Benefits of running models on-device
  • Local vs. cloud processing
  • Choosing the appropriate deployment approach

Real-World Applications Across Sectors

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

Integration Essentials

  • Evaluating technical requirements
  • Workflow and process implications
  • Overview of APIs and development tools

Cost Efficiency and Optimization

  • Cutting inference expenses with compact models
  • Optimizing performance vs. resource usage
  • Planning for scalable growth

Governance, Privacy, and Risk Control

  • Ensuring secure on-device operation
  • Navigating data boundaries and protection measures
  • Aligning with corporate policies and standards

Preparing for Enterprise Adoption

  • Building internal skills and preparedness
  • Evaluating business value via pilot projects
  • Laying the foundation for wider implementation

Summary and Future Steps

Requirements

  • Familiarity with general IT principles
  • Experience using basic software applications
  • Knowledge of data-centric business processes

Target Audience

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  • IT professionals implementing AI solutions
  • Business users interested in real-world AI applications
  • Tech managers evaluating on-device LLM strategies
 7 Hours

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