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Course Outline
Intro to Lightweight LLMs
- Grasping the architecture of compact models
- The progression of resource-efficient AI
- The importance of lightweight models for enterprises
Exploring Nano Banana
- Core features and underlying design principles
- Model strengths and inherent limitations
- Distinguishing Nano Banana from conventional LLMs
Deployment Strategies and Use Cases
- Benefits of on-device execution
- Comparing local versus cloud-based inference
- Choosing the optimal deployment route
Practical Applications in Various Industries
- Internal automation and knowledge support
- Scenarios involving customer interaction
- Situations driven by operational and compliance needs
Basics of Integration
- Assessing system requirements
- Considerations for workflows and processes
- Overview of APIs and toolchains
Optimizing Costs and Efficiency
- Lowering inference costs through compact models
- Striking a balance between performance and resource usage
- 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
Preparing for Organizational Implementation
- Developing internal expertise and readiness
- Evaluating business value via pilot initiatives
- Establishing the foundation for wider rollouts
Recap and Future Steps
Requirements
- A solid grasp of general IT concepts
- Basic experience with standard software tools
- Familiarity with data-driven business processes
Target Audience
- IT teams beginning to adopt AI capabilities
- Business professionals interested in practical AI applications
- Technology leaders evaluating on-device LLM strategies
7 Hours
Testimonials (1)
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