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Duration 7 hours
Course Outline
Foundations of Lightweight LLMs
- Comprehending compact model architectures
- The progression of resource-efficient AI technologies
- The significance of lightweight models for enterprise environments
Exploring Nano Banana
- Essential features and foundational design principles
- Model strengths and inherent limitations
- Distinguishing Nano Banana from conventional LLMs
Deployment Strategies and Application Scenarios
- The benefits of on-device execution
- Comparing local and cloud-based inference
- Choosing the most appropriate deployment approach
Industry-Specific Practical Uses
- Internal automation and knowledge support
- Customer-facing application cases
- Scenarios driven by operational needs and compliance
Basics of Integration
- Assessing system prerequisites
- Considerations for workflows and processes
- An introduction to APIs and toolchains
Cost Efficiency and Optimization
- Leveraging compact models to lower inference expenses
- Achieving a balance between performance and resource usage
- Strategizing for scalable deployments
Governance, Privacy, and Risk Control
- Guaranteeing secure execution on local devices
- Understanding data boundaries and protective measures
- Ensuring alignment with enterprise policies and standards
Readiness for Organizational Implementation
- Cultivating internal skills and preparedness
- Evaluating business value via pilot initiatives
- Establishing the foundation for wider adoption
Overview and Future Directions
Requirements
- A solid grasp of fundamental IT concepts
- Proficiency with basic software tools
- Familiarity with data-centric business processes
Target Audience
- IT teams integrating AI capabilities into their operations
- Business professionals interested in practical AI solutions
- Technology leaders evaluating strategies for on-device LLMs
Testimonials (1)
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