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Duration 7 hours
Course Outline
Introduction to Lightweight LLMs
- Comprehending compact model architectures
- The progression of resource-efficient AI
- The significance of lightweight models for enterprises
Understanding Nano Banana
- Core features and design principles
- Model capabilities and constraints
- Differences between Nano Banana and traditional LLMs
Deployment Models and Use Scenarios
- On-device execution and its advantages
- Comparison of local versus cloud inference
- Choosing the optimal deployment pathway
Practical Applications Across Industries
- Internal automation and knowledge support
- Customer-facing application scenarios
- Operational and compliance-focused use cases
Integration Fundamentals
- Assessing system requirements
- Considerations for workflows and processes
- Overview of APIs and toolchains
Cost Optimization and Efficiency
- Leveraging compact models to lower inference costs
- Balancing performance against resource usage
- Planning for scalable implementations
Governance, Privacy, and Risk Management
- Securing on-device execution
- Understanding data boundaries and protective measures
- Aligning with enterprise policies and standards
Preparing for Organizational Adoption
- Building internal capabilities and readiness
- Evaluating business value via pilot projects
- Establishing the foundation for wider rollout
Summary and Next Steps
Requirements
- A solid grasp of general IT concepts
- Proficiency with basic software tools
- Familiarity with data-centric business processes
Target Audience
- IT teams integrating AI capabilities
- Business professionals interested in practical AI applications
- Technology leaders assessing on-device LLM strategies
Testimonials (1)
Flow , vibe and topic on presentation
