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Duration 7 hours
Course Outline
Introduction to Lightweight LLMs
- Exploring compact model architectures
- The progression of resource-efficient AI
- The importance of lightweight models for enterprises
Getting to Know Nano Banana
- Core features and design philosophy
- Model strengths and constraints
- Distinctions between Nano Banana and conventional LLMs
Deployment Models and Use Cases
- Benefits of on-device execution
- Comparing local versus cloud inference
- Choosing the optimal deployment route
Practical Applications Across Sectors
- Internal automation and knowledge support
- Customer-oriented use cases
- Operational and compliance-focused scenarios
Integration Basics
- Reviewing system requirements
- Considerations for workflows and processes
- Intro to APIs and toolchains
Cost Efficiency and Optimization
- Lowering inference costs through compact models
- Striking a balance between performance and resources
- Planning for scalable deployments
Governance, Privacy, and Risk Control
- Safeguarding secure on-device execution
- Understanding data boundaries and protective measures
- Aligning with enterprise policies and standards
Readiness for Organizational Adoption
- Building internal competence and preparedness
- Evaluating business value via pilot projects
- Preparing the foundation for wider rollouts
Summary and Future Directions
Requirements
- A grasp of fundamental IT concepts
- Proficiency with basic software utilities
- Familiarity with data-centric business processes
Target Audience
- General IT teams looking to adopt AI functionalities
- Business professionals interested in practical AI implementations
- Tech leaders assessing strategies for on-device LLMs
Testimonials (1)
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