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Duration 7 hours
Course Outline
Introduction to Lightweight LLMs
- Comprehending compact model architectures
- The progression of resource-efficient AI
- The significance of lightweight models for the enterprise
Exploring Nano Banana
- Core features and design methodologies
- Model strengths and constraints
- Distinguishing Nano Banana from conventional LLMs
Deployment Strategies and Scenarios
- Advantages of on-device execution
- Comparing local versus cloud inference
- Choosing the optimal deployment approach
Real-World Applications Across Sectors
- Streamlining internal automation and knowledge support
- Implementing customer-facing solutions
- Addressing operational and compliance-driven needs
Integration Essentials
- Reviewing system requirements
- Considering workflow and process impacts
- Overview of APIs and toolchains
Cost Management and Efficiency
- Lowering inference expenses with compact models
- Optimizing the balance between performance and resources
- Strategizing for scalable implementations
Governance, Privacy, and Risk Oversight
- Safeguarding secure on-device operations
- Navigating data boundaries and protective measures
- Ensuring alignment with corporate policies and standards
Readiness for Organizational Integration
- Cultivating internal skills and preparedness
- Measuring business value via pilot initiatives
- Establishing the foundation for broader adoption
Summary and Future Directions
Requirements
- Foundational knowledge of general IT principles
- Proficiency with basic software tools
- Experience with data-driven business processes
Intended Audience
- General IT teams integrating AI capabilities
- Business users exploring practical AI solutions
- Technology leaders assessing on-device LLM strategies
Testimonials (1)
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