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Course Outline

Foundations of Lightweight LLMs

  • Comprehending compact model architectures
  • The progression of resource-efficient AI technologies
  • The significance of lightweight models for enterprise environments

Exploring Nano Banana

  • Essential features and foundational design principles
  • Model strengths and inherent limitations
  • Distinguishing Nano Banana from conventional LLMs

Deployment Strategies and Application Scenarios

  • The benefits of on-device execution
  • Comparing local and cloud-based inference
  • Choosing the most appropriate deployment approach

Industry-Specific Practical Uses

  • Internal automation and knowledge support
  • Customer-facing application cases
  • Scenarios driven by operational needs and compliance

Basics of Integration

  • Assessing system prerequisites
  • Considerations for workflows and processes
  • An introduction to APIs and toolchains

Cost Efficiency and Optimization

  • Leveraging compact models to lower inference expenses
  • Achieving a balance between performance and resource usage
  • Strategizing for scalable deployments

Governance, Privacy, and Risk Control

  • Guaranteeing secure execution on local devices
  • Understanding data boundaries and protective measures
  • Ensuring alignment with enterprise policies and standards

Readiness for Organizational Implementation

  • Cultivating internal skills and preparedness
  • Evaluating business value via pilot initiatives
  • Establishing the foundation for wider adoption

Overview and Future Directions

Requirements

  • A solid grasp of fundamental IT concepts
  • Proficiency with basic software tools
  • Familiarity with data-centric business processes

Target Audience

  • IT teams integrating AI capabilities into their operations
  • Business professionals interested in practical AI solutions
  • Technology leaders evaluating strategies for on-device LLMs
 7 Hours

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