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Course Outline

Introduction to Lightweight LLMs

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

Insights into Nano Banana

  • Core features and foundational design principles
  • Analyzing 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
  • Determining the optimal deployment approach

Industry-Specific Practical Applications

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

Integration Essentials

  • Reviewing system prerequisites
  • Considering workflow and process integration
  • Introduction to APIs and supporting toolchains

Cost Optimization and Efficiency

  • Leveraging compact models to lower inference expenses
  • Achieving a balance between performance and resource usage
  • Strategizing for scalable deployment initiatives

Governance, Privacy, and Risk Oversight

  • Safeguarding secure on-device operations
  • Defining data boundaries and protective measures
  • Ensuring alignment with enterprise policies and standards

Preparing for Organizational Implementation

  • Cultivating internal readiness and expertise
  • Evaluating business impact via pilot initiatives
  • Establishing the foundation for wider adoption

Recap and Future Directions

Requirements

  • A solid grasp of fundamental IT concepts
  • Proficiency with basic software tools
  • Knowledge of data-driven business processes

Target Audience

  • IT teams integrating AI capabilities into their stack
  • Business professionals seeking practical AI applications
  • Technology leaders assessing on-device LLM strategies
 7 Hours

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