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 Duration 7 hours

Course Outline

Introduction to Lightweight LLMs

  • Exploring compact model architectures
  • Tracing the development of resource-efficient AI
  • Understanding the significance of lightweight models for enterprises

Gaining Insight into Nano Banana

  • Reviewing key features and design philosophies
  • Examining model capabilities and inherent limitations
  • Comparing Nano Banana with traditional LLMs

Deployment Models and Application Scenarios

  • Leveraging on-device execution and its advantages
  • Contrasting local versus cloud-based inference
  • Choosing the optimal deployment strategy

Practical Applications Across Various Industries

  • Implementing internal automation and knowledge support
  • Developing customer-facing solutions
  • Addressing operational and compliance-driven needs

Integration Essentials

  • Assessing system requirements
  • Considering workflow and process implications
  • Introducing API and toolchain components

Cost Optimization and Efficiency

  • Minimizing inference expenses through compact models
  • Striking a balance between performance and resource usage
  • Planning for scalable deployment strategies

Governance, Privacy, and Risk Management

  • Safeguarding secure on-device operations
  • Comprehending data boundaries and protective measures
  • Ensuring alignment with enterprise policies and standards

Preparing for Organizational Adoption

  • Developing internal capabilities and readiness
  • Evaluating business impact through pilot projects
  • Establishing the foundation for wider implementation

Summary and Path Forward

Requirements

  • A foundational understanding of general IT concepts
  • Hands-on experience with basic software tools
  • Knowledge of data-driven business processes

Intended Audience

  • General IT teams looking to integrate AI capabilities
  • Business professionals interested in applying AI practically
  • Technology managers assessing on-device LLM strategies

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