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Course Outline

Foundations of Lightweight LLMs

  • Exploring compact model architectures
  • The progression of resource-efficient AI
  • The significance of lightweight models for enterprises

Exploring Nano Banana

  • Core features and design philosophy
  • Model strengths and constraints
  • Distinguishing Nano Banana from conventional LLMs

Deployment Strategies and Use Cases

  • On-device execution and its advantages
  • Comparing local and cloud-based inference
  • Choosing the optimal deployment approach

Industry-Specific Applications

  • Streamlining internal automation and knowledge support
  • Customer-centric use cases
  • Operational and compliance-focused scenarios

Integration Essentials

  • Assessing system prerequisites
  • Considerations for workflows and processes
  • Introduction to APIs and toolchains

Cost Efficiency and Optimization

  • Leveraging compact models to lower inference costs
  • Striking a balance between performance and resources
  • Planning for scalable implementations

Governance, Privacy, and Risk Oversight

  • Securing on-device operations
  • Comprehending data boundaries and protective measures
  • Adherence to enterprise policies and standards

Readiness for Organizational Rollout

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

Recap and Future Directions

Requirements

  • A solid grasp of general IT concepts
  • Basic experience with software tools
  • Knowledge of data-driven business processes

Target Audience

  • IT teams integrating AI capabilities
  • Business professionals interested in practical AI solutions
  • Technology leaders assessing on-device LLM strategies
 7 Hours

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