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Duration 7 hours
Course Outline
Introduction to Lightweight LLMs
- Exploring compact model architectures
- The evolution of resource-efficient AI
- The importance of lightweight models for enterprises
Understanding Nano Banana
- Core features and design principles
- Model capabilities and inherent limitations
- Distinguishing Nano Banana from traditional LLMs
Deployment Models and Use Scenarios
- On-device execution and its advantages
- Local versus cloud inference
- Choosing the optimal deployment strategy
Practical Applications Across Industries
- Internal automation and knowledge support
- Customer-facing applications
- Operational and compliance-focused scenarios
Integration Fundamentals
- Assessing system requirements
- Workflow and process implications
- Overview of APIs and toolchains
Cost Optimization and Efficiency
- Lowering inference costs with compact models
- Balancing performance against resource usage
- Planning for scalable deployments
Governance, Privacy, and Risk Management
- Securing on-device execution
- Data boundaries and protective measures
- Aligning with enterprise policies and standards
Preparing for Organizational Adoption
- Developing internal capabilities and readiness
- Evaluating business value through pilot projects
- Establishing the foundation for broader implementation
Summary and Next Steps
Requirements
- A general understanding of IT concepts
- Experience with basic software tools
- Familiarity with data-driven business workflows
Target Audience
- General IT teams implementing AI capabilities
- Business users interested in practical AI applications
- Technology managers evaluating on-device LLM strategies
Testimonials (1)
Flow , vibe and topic on presentation
