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 Duration 7 hours

Course Outline

Foundations of Lightweight LLMs

  • Exploring compact model architectures
  • The progression of resource-efficient AI technologies
  • The significance of lightweight models in enterprise environments

Deep Dive into Nano Banana

  • Core features and underlying design philosophies
  • Analysis of model strengths and constraints
  • Distinguishing Nano Banana from conventional LLMs

Deployment Strategies and Scenarios

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

Industry-Specific Applications

  • Enhancing internal automation and knowledge support
  • Implementing customer-facing AI solutions
  • Addressing operational and compliance-related needs

Essentials of Integration

  • Reviewing system prerequisites
  • Considering workflow and process implications
  • Overview of API and toolchain components

Optimizing Cost and Efficiency

  • Lowering inference expenses via compact models
  • Striking a balance between performance and resource usage
  • Designing for scalable deployment

Governance, Privacy, and Risk

  • Safeguarding secure on-device operations
  • Comprehending data boundaries and protective measures
  • Aligning with corporate policies and industry standards

Facilitating Organizational Adoption

  • Cultivating internal skills and readiness
  • Determining business value via pilot initiatives
  • Preparing the infrastructure for wider implementation

Recap and Future Directions

Requirements

  • Basic knowledge of general IT principles
  • Practical experience with standard software tools
  • Working familiarity with data-centric business processes

Target Audience

  • IT teams integrating AI capabilities into their operations
  • Business professionals interested in applying practical AI solutions
  • Technology leaders assessing strategies for on-device LLM implementation

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