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Course Outline

Introduction to Lightweight LLMs

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

Getting to Know Nano Banana

  • Core features and underlying design philosophy
  • Defining model strengths and constraints
  • Distinguishing Nano Banana from conventional LLMs

Deployment Strategies and Use Cases

  • Advantages of on-device execution
  • Comparing local and cloud-based inference
  • Choosing the optimal deployment approach

Real-World Applications in Various Sectors

  • Streamlining internal operations and providing knowledge support
  • Customer-centric use cases
  • Scenarios driven by operational needs and compliance

Fundamentals of Integration

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

Cost Reduction and Efficiency

  • Lowering inference expenses through compact models
  • Optimizing the balance between performance and resources
  • Designing for scalable implementations

Governance, Data Privacy, and Risk Control

  • Safeguarding on-device execution
  • Navigating data limits and security measures
  • Ensuring compliance with enterprise standards and policies

Preparing for Organizational Implementation

  • Developing internal skills and readiness
  • Validating business impact through pilot initiatives
  • Establishing a foundation for widespread adoption

Wrap-Up and Future Directions

Requirements

  • A solid grasp of fundamental IT principles
  • Proficiency with basic software tools
  • Knowledge of data-driven business processes

Target Audience

  • IT teams looking to integrate AI capabilities
  • Business professionals seeking practical AI solutions
  • Technology leaders assessing on-device LLM strategies
 7 Hours

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