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Course Outline

Foundations of Lightweight LLMs

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

The Nano Banana Framework

  • Core features and underlying design principles
  • Capabilities and inherent limitations of the model
  • Distinguishing Nano Banana from conventional LLMs

Deployment Strategies and Scenarios

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

Industry-Specific Practical Applications

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

Integration Essentials

  • Assessing technical system requirements
  • Considerations for workflow and process integration
  • Introduction to APIs and relevant toolchains

Cost Optimization and Performance

  • Leveraging compact models to lower inference expenses
  • Balancing performance against resource consumption
  • Strategies for scalable deployment planning

Governance, Privacy, and Risk Control

  • Securing on-device execution environments
  • Managing data boundaries and protective measures
  • Ensuring alignment with corporate policies and standards

Strategic Organizational Adoption

  • Developing internal expertise and readiness
  • Measuring business impact through pilot initiatives
  • Preparing the foundation for broader implementation

Recap and Forward Planning

Requirements

  • Familiarity with fundamental IT principles
  • Experience using basic software tools
  • Understanding of data-driven business processes

Target Audience

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

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