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Course Outline

Foundations of Lightweight LLMs

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

Delving into Nano Banana

  • Essential features and design principles
  • Model capabilities and constraints
  • Distinguishing Nano Banana from conventional LLMs

Deployment Models and Application Scenarios

  • On-device execution and its advantages
  • Local versus cloud-based inference
  • Choosing the optimal deployment approach

Practical Applications in Various Industries

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

Integration Basics

  • Assessing system requirements
  • Workflow and process implications
  • Introduction to APIs and toolchains

Cost Optimization and Efficiency

  • Leveraging compact models to lower inference costs
  • Balancing performance with resource usage
  • Planning for scalable deployments

Governance, Privacy, and Risk Control

  • Safeguarding secure on-device execution
  • Comprehending data boundaries and protections
  • Aligning with enterprise policies and standards

Readiness for Organizational Implementation

  • Developing internal competencies and readiness
  • Evaluating business value via pilot projects
  • Establishing the foundation for wider adoption

Conclusion and Future Steps

Requirements

  • Knowledge of general IT concepts
  • Experience with foundational software tools
  • Familiarity with data-driven business workflows

Target Audience

  • General IT teams integrating AI capabilities
  • Business users interested in practical AI implementations
  • Technology managers evaluating on-device LLM strategies
 7 Hours

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