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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 the enterprise context

Insights into Nano Banana

  • Core features and architectural design principles
  • Analyzing model strengths and constraints
  • Distinguishing Nano Banana from conventional LLMs

Deployment Strategies and Application Scenarios

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

Real-World Industry Applications

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

Integration Essentials

  • Reviewing necessary system requirements
  • Considering workflow and process impacts
  • Introduction to APIs and supporting toolchains

Optimizing Costs and Efficiency

  • Lowering inference expenses through compact models
  • Striking a balance between performance and resource usage
  • Planning for scalable implementation

Governance, Data Privacy, and Risk Mitigation

  • Securing on-device execution environments
  • Comprehending data boundaries and protective measures
  • Ensuring alignment with corporate policies and regulatory standards

Readying for Organizational Integration

  • Developing internal skills and operational readiness
  • Evaluating business impact via pilot initiatives
  • Establishing the basis for wider adoption

Recap and Future Actions

Requirements

  • Proficiency in general IT fundamentals
  • Experience utilizing basic software applications
  • Awareness of data-centric business processes

Target Audience

  • IT teams integrating AI functionalities into their operations
  • Business professionals interested in the practical application of AI
  • Technology leaders assessing on-device LLM strategies

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