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Course Outline

Foundations of Lightweight LLMs

  • Exploring compact model structures
  • The trajectory of resource-optimized AI
  • The significance of lightweight models for the enterprise

Deep Dive into Nano Banana

  • Core features and architectural principles
  • Assessing model strengths and constraints
  • Distinguishing Nano Banana from conventional LLMs

Deployment Strategies and Application Scenarios

  • Benefits of on-device processing
  • Comparing local vs. cloud-based inference
  • Determining the optimal deployment approach

Cross-Industry Practical Uses

  • Streamlining internal automation and knowledge support
  • Customer interaction enhancements
  • Scenarios driven by operations and compliance requirements

Integration Essentials

  • Reviewing system prerequisites
  • Considerations for workflow and process alignment
  • Overview of APIs and toolchains

Cost Efficiency and Performance Optimization

  • Lowering inference expenses through compact models
  • Striking a balance between performance and resource usage
  • Strategizing for scalable implementation

Governance, Data Privacy, and Risk Control

  • Safeguarding on-device execution security
  • Defining data boundaries and protective measures
  • Aligning with corporate policies and regulatory standards

Readiness for Organizational Implementation

  • Cultivating internal skills and preparedness
  • Measuring business impact via pilot initiatives
  • Establishing the foundation for wider adoption

Wrap-up and Future Directions

Requirements

  • A foundational grasp of general IT principles
  • Proficiency with basic software utilities
  • Knowledge of data-centric business processes

Target Audience

  • IT teams integrating AI functionalities
  • Business stakeholders exploring practical AI solutions
  • Technology leaders assessing on-device LLM strategies
 7 Hours

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