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Course Outline

Introduction to Lightweight LLMs

  • Comprehending compact model structures
  • The progression of resource-optimized AI
  • The significance of lightweight models for enterprise sectors

Exploring Nano Banana

  • Core attributes and design foundations
  • Assessing model strengths and constraints
  • Distinguishing Nano Banana from conventional LLMs

Deployment Strategies and Scenarios

  • Advantages of on-device processing
  • Comparing local versus cloud-based inference
  • Choosing the optimal deployment route

Real-World Applications in Various Industries

  • Streamlining internal processes and providing knowledge support
  • Implementing customer-centric solutions
  • Addressing operational and regulatory requirements

Fundamentals of Integration

  • Reviewing technical system prerequisites
  • Considering workflow and procedural impacts
  • Overview of API and toolchain components

Cost Reduction and Efficiency

  • Lowering inference expenses through compact models
  • Optimizing the balance between performance and resource usage
  • Planning for expandable deployment architectures

Governance, Data Privacy, and Risk Oversight

  • Safeguarding secure on-device operations
  • Understanding data limits and protective measures
  • Ensuring alignment with corporate policies and standards

Readiness for Organizational Implementation

  • Developing internal skills and preparation
  • Determining business value via pilot initiatives
  • Establishing the foundation for wider adoption

Summary and Future Directions

Requirements

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

Target Audience

  • IT departments integrating AI functionalities
  • Business professionals seeking practical AI solutions
  • Technical leaders assessing on-device LLM strategies
 7 Hours

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