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Course Outline

Foundations of Lightweight LLMs

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

Deep Dive into Nano Banana

  • Core features and underlying design principles
  • Understanding model strengths and constraints
  • Distinguishing Nano Banana from conventional LLMs

Deployment Strategies and Application Scenarios

  • Advantages of on-device execution
  • Comparing local versus cloud-based inference
  • Determining the optimal deployment approach

Industry-Specific Practical Applications

  • Internal automation and knowledge support tools
  • Customer-facing integration examples
  • Scenarios driven by operational and compliance needs

Integration Essentials

  • Reviewing system prerequisites
  • Considering workflow and procedural impacts
  • Introduction to APIs and supporting toolchains

Cost Management and Operational Efficiency

  • Minimizing inference expenses through compact models
  • Optimizing the balance between performance and resources
  • Strategizing for scalable implementations

Governance, Privacy, and Risk Oversight

  • Securing on-device operations
  • Comprehending data limits and protective measures
  • Aligning with corporate policies and industry standards

Facilitating Organizational Adoption

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

Recap and Future Actions

Requirements

  • A foundational grasp of general IT principles
  • Experience using basic software applications
  • Knowledge of data-centric business processes

Intended Audience

  • IT teams seeking to adopt AI capabilities
  • Business professionals interested in practical AI uses
  • Technology leaders assessing strategies for on-device LLMs
 7 Hours

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