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Duration 7 hours
Course Outline
Basics of Lightweight LLMs
- Comprehending compact model structures
- The progression of resource-optimized AI
- The significance of lightweight models for enterprises
Exploring Nano Banana
- Core features and architectural philosophy
- Strengths and constraints of the model
- Differences between Nano Banana and conventional LLMs
Deployment Strategies and Application Scenarios
- Advantages of on-device processing
- Comparing local versus cloud-based inference
- Choosing the optimal deployment approach
Real-World Applications in Various Sectors
- Internal automation and knowledge support
- Use cases facing end-users
- Scenarios driven by operations and compliance
Essentials of Integration
- Reviewing system prerequisites
- Considerations for workflows and processes
- Introduction to APIs and toolchains
Cost Efficiency and Optimization
- Lowering inference expenses through compact models
- Striking a balance between performance and resource usage
- Planning for scalable implementation
Governance, Privacy, and Risk Oversight
- Securing on-device operations
- Understanding data limits and protective measures
- Adhering to enterprise policies and benchmarks
Preparing for Organizational Implementation
- Developing internal skills and readiness
- Evaluating business impact via pilot initiatives
- Preparing for wider organizational rollout
Recap and Future Directions
Requirements
- A solid grasp of general IT principles
- Proficiency with fundamental software tools
- Knowledge of data-centric business processes
Intended Audience
- General IT teams integrating AI capabilities
- Business professionals interested in applied AI solutions
- Technology managers assessing on-device LLM strategies
Testimonials (1)
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