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Duration 7 hours
Course Outline
Introduction to Lightweight LLMs
- Exploring compact model architectures
- The progression of resource-efficient AI
- The significance of lightweight models for enterprises
Comprehending Nano Banana
- Core features and underlying design principles
- Model strengths and inherent limitations
- Distinguishing Nano Banana from conventional LLMs
Deployment Models and Application Scenarios
- On-device execution and its associated advantages
- Comparing local versus cloud-based inference
- Determining the optimal deployment pathway
Practical Applications Across Sectors
- Internal automation and knowledge support systems
- Customer-facing applications
- Operational and compliance-focused use cases
Integration Fundamentals
- Assessing system requirements
- Workflow and procedural considerations
- Introduction to APIs and the toolchain
Cost Optimization and Efficiency
- Lowering inference costs through compact models
- Striking a balance between performance and resource usage
- Planning for scalable deployment strategies
Governance, Privacy, and Risk Mitigation
- Securing on-device execution environments
- Understanding data boundaries and protective measures
- Aligning with enterprise policies and industry standards
Preparing for Organizational Adoption
- Developing internal capabilities and readiness
- Evaluating business impact through pilot initiatives
- Establishing a foundation for wider implementation
Summary and Future Directions
Requirements
- A solid grasp of general IT fundamentals
- Proficiency with basic software tools
- Working knowledge of data-driven business processes
Target Audience
- IT teams integrating AI capabilities into their operations
- Business professionals interested in practical AI implementations
- Technology managers assessing on-device LLM strategies
Testimonials (1)
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