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Duration 7 hours
Course Outline
Fundamentals of 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 philosophies
- Model strengths and constraints
- Distinguishing Nano Banana from conventional LLMs
Deployment Strategies and Application Scenarios
- On-device processing and its advantages
- Comparing local versus cloud-based inference
- Choosing the optimal deployment route
Industry-Specific Practical Applications
- Automating internal tasks and providing knowledge support
- Client-facing application examples
- Scenarios driven by operations and compliance
Essentials of Integration
- Assessing system prerequisites
- Considerations for workflows and processes
- Overview of APIs and toolchains
Optimizing Costs and Efficiency
- Lowering inference expenses through compact models
- Striking a balance between performance and resources
- Strategizing for scalable implementations
Governance, Privacy, and Risk Oversight
- Guaranteeing secure on-device execution
- Grasping data limits and protective measures
- Aligning with enterprise policies and standards
Readying the Organization for Adoption
- Cultivating internal skills and preparedness
- Measuring business impact via pilot initiatives
- Establishing the foundation for wider implementation
Wrap-up and Future Steps
Requirements
- A grasp of general IT principles
- Proficiency with fundamental software tools
- Acquaintance with data-centric business processes
Target Audience
- IT teams looking to adopt AI capabilities
- Business professionals interested in practical AI implementations
- Technology leaders assessing on-device LLM strategies
Testimonials (1)
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