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Course Outline

Foundations of Lightweight LLMs

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

Diving Deeper into Nano Banana

  • Core features and architectural design principles
  • Analysis of model strengths and constraints
  • Distinguishing Nano Banana from conventional LLMs

Deployment Strategies and Scenarios

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

Cross-Industry Practical Applications

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

Integration Essentials

  • Reviewing system requirements
  • Considerations for workflow and process integration
  • Introduction to APIs and the development toolchain

Cost Optimization and Efficiency

  • Leveraging compact models to lower inference expenses
  • Striking a balance between performance and resource usage
  • Planning for scalable implementation

Governance, Privacy, and Risk Oversight

  • Securing on-device execution environments
  • Navigating data boundaries and protection measures
  • Aligning with corporate policies and industry standards

Strategies for Organizational Adoption

  • Developing internal skills and readiness
  • Measuring business impact through pilot initiatives
  • Establishing the foundation for wider rollout

Wrap-up and Recommended Next Steps

Requirements

  • A general grasp of IT fundamentals
  • Experience with basic software applications
  • Knowledge of data-centric business processes

Target Audience

  • IT teams looking to adopt AI capabilities
  • Business users seeking practical AI solutions
  • Technology managers assessing on-device LLM strategies
 7 Hours

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