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Duration 7 hours
Course Outline
Introduction to Lightweight LLMs
- Understanding compact model architectures
- The evolution of resource-efficient AI
- Why lightweight models matter for enterprises
Understanding Nano Banana
- Key features and design principles
- Model capabilities and limitations
- How Nano Banana differs from traditional LLMs
Deployment Models and Use Scenarios
- On-device execution and its benefits
- Local versus cloud inference
- Selecting the right deployment path
Practical Applications Across Industries
- Internal automation and knowledge assistance
- Customer-facing use cases
- Operational and compliance-driven scenarios
Integration Fundamentals
- Evaluating system requirements
- Workflow and process considerations
- API and toolchain introduction
Cost Optimization and Efficiency
- Reducing inference costs using compact models
- Balancing performance and resources
- Planning for scalable deployments
Governance, Privacy, and Risk Management
- Ensuring secure on-device execution
- Understanding data boundaries and safeguards
- Alignment with enterprise policies and standards
Preparing for Organizational Adoption
- Building internal capability and readiness
- Assessing business value through pilot projects
- Laying the groundwork for broader rollouts
Summary and Next Steps
Requirements
- General understanding of IT concepts
- Experience using basic software tools
- Familiarity with data-driven business workflows
Target Audience
- General IT teams integrating AI capabilities
- Business professionals interested in practical AI applications
- Technology managers assessing on-device LLM strategies
Testimonials (1)
Flow , vibe and topic on presentation
