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Duration 7 hours
Course Outline
Foundations of Lightweight LLMs
- Exploring compact model architectures
- The progression of resource-efficient AI
- The importance of lightweight models for enterprise use
Navigating Nano Banana
- Core features and design philosophy
- Understanding model strengths and constraints
- Distinguishing Nano Banana from conventional LLMs
Deployment Strategies and Application Scenarios
- Advantages of on-device execution
- Comparing local versus cloud inference
- Choosing the optimal deployment route
Cross-Industry Practical Uses
- Internal automation and knowledge support
- Customer-centric applications
- Operational and compliance-related scenarios
Integration Basics
- Reviewing system requirements
- Considering workflow and process impacts
- Introduction to APIs and toolchains
Cost Efficiency and Optimization
- Leveraging compact models to lower inference expenses
- Balancing performance against resource usage
- Planning for scalable implementations
Governance, Privacy, and Risk Control
- Securing on-device operations
- Understanding data boundaries and protective measures
- Aligning with enterprise policies and standards
Preparing for Organizational Implementation
- Building internal skills and readiness
- Evaluating business value via pilot initiatives
- Preparing for wider organizational adoption
Conclusion and Future Steps
Requirements
- Basic knowledge of general IT concepts
- Experience using fundamental software tools
- Acquaintance with data-driven business processes
Target Audience
- IT teams integrating AI capabilities
- Business professionals interested in practical AI uses
- Technology managers evaluating on-device LLM strategies
Testimonials (1)
Flow , vibe and topic on presentation
