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Course Outline

Foundations of Lightweight LLMs

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

Insight into Nano Banana

  • Core features and design philosophy
  • Understanding model capabilities and constraints
  • Distinguishing Nano Banana from traditional LLMs

Deployment Strategies and Use Cases

  • Benefits of on-device execution
  • Comparing local versus cloud inference
  • Choosing the optimal deployment path

Practical Industry Applications

  • Internal automation and knowledge support
  • Customer-facing applications
  • Operational and compliance-focused scenarios

Integration Basics

  • Assessing system requirements
  • Considering workflow and process impacts
  • Introduction to APIs and toolchains

Optimizing Costs and Efficiency

  • Lowering inference costs through compact models
  • Balancing performance against resource usage
  • Planning for scalable implementations

Governance, Privacy, and Risk Oversight

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

Readiness for Organizational Adoption

  • Building internal competence and preparedness
  • Evaluating business value via pilot projects
  • Establishing the foundation for wider rollouts

Conclusions and Future Directions

Requirements

  • A solid understanding of general IT concepts
  • Basic proficiency with standard software tools
  • Familiarity with data-driven business workflows

Target Audience

  • IT teams looking to adopt AI capabilities
  • Business users interested in practical AI applications
  • Technology managers evaluating on-device LLM strategies
 7 Hours

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