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Duration 7 hours
Course Outline
Fundamentals of Lightweight LLMs
- Examining compact model architectures
- The progression of resource-efficient AI
- The strategic importance of lightweight models for enterprises
Exploring Nano Banana
- Core features and architectural design
- Capabilities and inherent limitations of the model
- Distinguishing Nano Banana from conventional LLMs
Deployment Strategies and Scenarios
- Advantages of on-device execution
- Comparing local and cloud-based inference
- Selecting the optimal deployment pathway
Industry-Specific Applications
- Internal automation and knowledge support
- Customer engagement use cases
- Operational and compliance-oriented scenarios
Integration Basics
- Assessing system requirements
- 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 implementations
Governance, Privacy, and Risk Control
- Safeguarding on-device execution
- Defining data boundaries and protective measures
- Aligning with enterprise policies and standards
Readiness for Organizational Implementation
- Developing internal skills and preparedness
- Measuring business value via pilot initiatives
- Establishing the foundation for broader deployment
Conclusion and Future Actions
Requirements
- General knowledge of IT concepts
- Basic proficiency with software tools
- Understanding of data-driven business workflows
Target Audience
- IT teams integrating AI capabilities
- Business professionals interested in practical AI applications
- Technology managers assessing on-device LLM strategies
Testimonials (1)
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