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Duration 7 hours
Course Outline
Foundations of Lightweight LLMs
- Exploring compact model structures
- The progression of resource-optimized AI
- The importance of lightweight models for enterprise needs
Insights into Nano Banana
- Primary features and architectural principles
- Scope of model abilities and constraints
- Distinguishing Nano Banana from conventional LLMs
Deployment Strategies and Application Scenarios
- Advantages of on-device processing
- Comparing local and cloud-based inference
- Choosing the optimal deployment approach
Real-World Applications Across Sectors
- Streamlining internal operations and knowledge support
- Implementing customer-facing solutions
- Addressing operational and regulatory requirements
Basics of Integration
- Reviewing system prerequisites
- Considering workflow and process impacts
- Introduction to APIs and the toolchain
Optimizing Costs and Efficiency
- Lowering inference expenses via compact models
- Striking a balance between performance and resource usage
- Strategizing for scalable implementations
Governance, Confidentiality, and Risk Control
- Safeguarding secure on-device operations
- Defining data limits and protective measures
- Aligning with corporate policies and standards
Readiness for Organizational Implementation
- Developing internal competence and preparedness
- Measuring business impact via pilot initiatives
- Establishing the foundation for wider adoption
Recap and Future Actions
Requirements
- A foundational grasp of general IT principles
- Hands-on experience with standard software utilities
- Awareness of data-centric business processes
Target Audience
- IT teams integrating AI capabilities
- Business stakeholders seeking practical AI applications
- Technology leaders assessing strategies for on-device LLM adoption
Testimonials (1)
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