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Course Outline

Introduction to Lightweight LLMs

  • Comprehending compact model architectures.
  • The evolution of resource-efficient AI.
  • The significance of lightweight models for enterprises.

Understanding Nano Banana

  • Core features and design principles.
  • Model capabilities and limitations.
  • Differentiating Nano Banana from traditional LLMs.

Deployment Models and Use Scenarios

  • Benefits of on-device execution.
  • Comparing local versus cloud inference.
  • Selecting the appropriate deployment path.

Practical Applications Across Industries

  • Internal automation and knowledge support.
  • Customer-facing use cases.
  • Operational and compliance-driven scenarios.

Integration Fundamentals

  • Evaluating system requirements.
  • Considerations for workflow and process design.
  • Introduction to API and toolchain components.

Cost Optimization and Efficiency

  • Reducing inference costs through compact models.
  • Striking a balance between performance and resources.
  • 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.
  • Establishing a foundation for broader rollouts.

Summary and Next Steps

Requirements

  • Familiarity with fundamental IT concepts.
  • Experience using basic software tools.
  • Understanding of data-driven business workflows.

Target Audience

  • IT teams integrating AI capabilities.
  • Business professionals interested in practical AI applications.
  • Technology managers evaluating on-device LLM strategies.
 7 Hours

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