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Duration 7 hours
Course Outline
Foundations of Lightweight LLMs
- Exploring compact model architectures
- The progression of resource-efficient AI technologies
- The significance of lightweight models in enterprise environments
Deep Dive into Nano Banana
- Core features and underlying design philosophies
- Analysis of model strengths and constraints
- Distinguishing Nano Banana from conventional LLMs
Deployment Strategies and Scenarios
- Advantages of on-device execution
- Comparing local versus cloud-based inference
- Choosing the optimal deployment route
Industry-Specific Applications
- Enhancing internal automation and knowledge support
- Implementing customer-facing AI solutions
- Addressing operational and compliance-related needs
Essentials of Integration
- Reviewing system prerequisites
- Considering workflow and process implications
- Overview of API and toolchain components
Optimizing Cost and Efficiency
- Lowering inference expenses via compact models
- Striking a balance between performance and resource usage
- Designing for scalable deployment
Governance, Privacy, and Risk
- Safeguarding secure on-device operations
- Comprehending data boundaries and protective measures
- Aligning with corporate policies and industry standards
Facilitating Organizational Adoption
- Cultivating internal skills and readiness
- Determining business value via pilot initiatives
- Preparing the infrastructure for wider implementation
Recap and Future Directions
Requirements
- Basic knowledge of general IT principles
- Practical experience with standard software tools
- Working familiarity with data-centric business processes
Target Audience
- IT teams integrating AI capabilities into their operations
- Business professionals interested in applying practical AI solutions
- Technology leaders assessing strategies for on-device LLM implementation
Testimonials (1)
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