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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

Gaining Insight into Nano Banana

  • Core features and underlying design principles
  • Defining model strengths and inherent limitations
  • Distinguishing Nano Banana from conventional LLMs

Deployment Strategies and Use Cases

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

Industry-Specific Applications

  • Automating internal processes and providing knowledge support
  • Implementing customer-facing AI solutions
  • Addressing operational and compliance-related needs

Core Integration Concepts

  • Assessing necessary system requirements
  • Considering impacts on workflows and processes
  • Introducing relevant APIs and toolchains

Optimizing Costs and Efficiency

  • Leveraging compact models to lower inference expenses
  • Striking a balance between performance and resource usage
  • Strategizing for scalable implementations

Compliance, Privacy, and Risk Oversight

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

Readiness for Organizational Implementation

  • Cultivating internal skills and organizational preparedness
  • Measuring business impact via pilot initiatives
  • Establishing the foundation for wider deployment

Recap and Future Directions

Requirements

  • A solid grasp of foundational IT concepts
  • Working knowledge of basic software tools
  • Exposure to data-centric business processes

Target Audience

  • IT teams incorporating AI capabilities into their workflows
  • Business stakeholders seeking practical AI solutions
  • Technology leaders assessing strategies for on-device LLMs

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