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Course Outline

Introduction to Lightweight LLMs

  • Comprehending compact model architectures
  • Tracing the evolution of resource-efficient AI
  • Understanding why lightweight models are critical for enterprises

Understanding Nano Banana

  • Exploring key features and design principles
  • Analyzing model capabilities and inherent limitations
  • Distinguishing Nano Banana from traditional LLMs

Deployment Models and Use Scenarios

  • Leveraging on-device execution and its advantages
  • Comparing local versus cloud inference strategies
  • Choosing the optimal deployment path

Practical Applications Across Industries

  • Implementing internal automation and knowledge assistance
  • Developing customer-facing AI use cases
  • Addressing operational and compliance-driven scenarios

Integration Fundamentals

  • Evaluating necessary system requirements
  • Considering workflow and process implications
  • Introduction to APIs and toolchains

Cost Optimization and Efficiency

  • Lowering inference costs through compact models
  • Balancing performance with resource utilization
  • Planning for scalable deployments

Governance, Privacy, and Risk Management

  • Ensuring secure on-device execution
  • Understanding data boundaries and safeguards
  • Aligning with enterprise policies and standards

Preparing for Organizational Adoption

  • Building internal capability and readiness
  • Assessing business value through pilot projects
  • Laying the foundation for broader rollouts

Summary and Next Steps

Requirements

  • Knowledge of fundamental IT principles
  • Hands-on experience with standard software applications
  • Understanding of data-oriented business processes

Intended Audience

  • IT teams integrating AI capabilities
  • Business stakeholders exploring practical AI solutions
  • Technical leaders assessing on-device LLM strategies
 7 Hours

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