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Course Outline

Overview of Lightweight LLMs

  • Grasping the architecture of compact models
  • The history of resource-efficient artificial intelligence
  • The importance of lean models for enterprise adoption

Deep Dive into Nano Banana

  • Core features and design philosophy
  • Capabilities and constraints of the model
  • Differences between Nano Banana and conventional LLMs

Deployment Models and Use Cases

  • Benefits of executing models directly on devices
  • Comparing local inference with cloud-based processing
  • Choosing the appropriate deployment strategy

Real-World Applications Across Sectors

  • Internal automation and knowledge support
  • Customer-facing interactions
  • Operational efficiency and compliance requirements

Essentials of Integration

  • Assessing technical system needs
  • Considering workflow and process impacts
  • Introduction to APIs and development tools

Cost Optimization and Performance

  • Lowering inference expenses with compact models
  • Optimizing the balance between performance and resource usage
  • Strategizing for scalable implementations

Governance, Privacy, and Risk Management

  • Ensuring security during on-device execution
  • Defining data boundaries and protective measures
  • Aligning with corporate policies and industry standards

Readying Your Organization for Adoption

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  • Developing internal skills and preparedness
  • Evaluating business impact through pilot initiatives
  • Establishing a foundation for wider implementation

Conclusion and Future Actions

Requirements

  • Basic knowledge of IT fundamentals
  • Experience using standard software applications
  • Understanding of data-centric business processes

Target Audience

  • IT teams looking to implement AI tools
  • Business professionals seeking practical AI solutions
  • Technology leaders assessing strategies for on-device LLMs
 7 Hours

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