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Duration 7 hours
Course Outline
Overview of Lightweight LLMs
- Grasping compact model architectures
- The progression of resource-efficient AI
- The significance of lightweight models for enterprises
Insights into Nano Banana
- Core features and design philosophy
- Operational capabilities and boundaries
- Distinctions between Nano Banana and conventional LLMs
Deployment Strategies and Scenarios
- Advantages of on-device execution
- Comparing local versus cloud-based inference
- Choosing the optimal deployment route
Practical Applications Across Sectors
- Internal automation and knowledge support
- Client-facing implementations
- Operational and compliance-focused uses
Integration Basics
- Assessing system requirements
- Considerations for workflows and processes
- Introduction to APIs and toolchains
Cost Efficiency and Optimization
- Lowering inference expenses via compact models
- Striking a balance between performance and resources
- Planning for scalable implementations
Governance, Privacy, and Risk Oversight
- Safeguarding secure on-device operations
- Understanding data limits and protections
- Aligning with enterprise policies and standards
Preparing for Organizational Implementation
- Developing internal skills and readiness
- Evaluating business impact through pilot initiatives
- Establishing a foundation for wider adoption
Summary and Future Directions
Requirements
- A grasp of general IT principles
- Practical experience with basic software tools
- Awareness of data-driven business processes
Intended Audience
- IT teams looking to adopt AI capabilities
- Business professionals interested in practical AI solutions
- Technology managers assessing on-device LLM strategies
Testimonials (1)
Flow , vibe and topic on presentation
