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Course Outline

Foundations of Lightweight LLMs

  • Exploring compact model structures
  • The progression of resource-optimized AI
  • The importance of lightweight models for enterprise needs

Insights into Nano Banana

  • Primary features and architectural principles
  • Scope of model abilities and constraints
  • Distinguishing Nano Banana from conventional LLMs

Deployment Strategies and Application Scenarios

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

Real-World Applications Across Sectors

  • Streamlining internal operations and knowledge support
  • Implementing customer-facing solutions
  • Addressing operational and regulatory requirements

Basics of Integration

  • Reviewing system prerequisites
  • Considering workflow and process impacts
  • Introduction to APIs and the toolchain

Optimizing Costs and Efficiency

  • Lowering inference expenses via compact models
  • Striking a balance between performance and resource usage
  • Strategizing for scalable implementations

Governance, Confidentiality, and Risk Control

  • Safeguarding secure on-device operations
  • Defining data limits and protective measures
  • Aligning with corporate policies and standards

Readiness for Organizational Implementation

  • Developing internal competence and preparedness
  • Measuring business impact via pilot initiatives
  • Establishing the foundation for wider adoption

Recap and Future Actions

Requirements

  • A solid grasp of fundamental IT concepts
  • Proficiency with basic software utilities
  • Exposure to data-centric business processes

Target Audience

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

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