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Duration 7 hours
Course Outline
Foundations of Lightweight LLMs
- Exploring compact model architectures
- The progression of resource-efficient AI
- The significance of lightweight models in enterprise settings
Navigating Nano Banana
- Core features and design philosophy
- Understanding model capabilities and constraints
- Distinguishing Nano Banana from conventional LLMs
Deployment Strategies and Scenarios
- On-device execution and its associated advantages
- Comparing local versus cloud inference
- Determining the optimal deployment pathway
Industry-Specific Practical Applications
- Streamlining internal automation and knowledge support
- Implementing customer-facing AI use cases
- Addressing operational and compliance-driven needs
Essentials of Integration
- Reviewing system requirements
- Considering workflow and process implications
- Introduction to APIs and the associated toolchain
Cost Optimization and Efficiency
- Lowering inference costs through compact models
- Balancing performance against resource usage
- Strategizing for scalable deployment
Governance, Privacy, and Risk Oversight
- Securing on-device execution environments
- Comprehending data boundaries and protective measures
- Ensuring alignment with enterprise policies and standards
Readiness for Organizational Adoption
- Cultivating internal competence and preparedness
- Measuring business value via pilot initiatives
- Establishing the foundation for extensive rollout
Recap and Future Directions
Requirements
- Working knowledge of general IT concepts
- Familiarity with basic software tools
- Experience with data-driven business workflows
Target Audience
- IT teams integrating AI capabilities into their operations
- Business users exploring practical AI solutions
- Technology managers assessing on-device LLM strategies
Testimonials (1)
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