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Course Outline

Overview of Lightweight LLMs

  • Exploring compact model architectures
  • The progression of resource-efficient AI
  • The importance of lightweight models for enterprises

Getting to Know Nano Banana

  • Core features and design philosophy
  • Model strengths and constraints
  • Distinguishing Nano Banana from conventional LLMs

Deployment Strategies and Application Scenarios

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

Real-World Applications Across Sectors

  • Internal automation and knowledge support
  • Customer-facing implementations
  • Operational and compliance-focused use cases

Essentials of Integration

  • Assessing system prerequisites
  • Considering workflow and process implications
  • An introduction to APIs and toolchains

Cost Efficiency and Optimization

  • Lowering inference expenses with compact models
  • Striking a balance between performance and resource usage
  • Planning for scalable implementations

Governance, Privacy, and Risk Control

  • Ensuring secure on-device operations
  • Comprehending data boundaries and protective measures
  • Aligning with corporate policies and standards

Readiness for Organizational Implementation

  • Developing internal skills and preparedness
  • Evaluating business impact through pilot initiatives
  • Establishing the foundation for wider adoption

Wrap-up and Future Directions

Requirements

  • A solid grasp of general IT concepts
  • Familiarity with basic software tools
  • An understanding of data-driven business processes

Target Audience

  • General IT teams integrating AI capabilities
  • Business professionals seeking practical AI applications
  • Technology leaders evaluating on-device LLM strategies
 7 Hours

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