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Duration 7 hours
Course Outline
Introduction to Lightweight LLMs
- Exploring compact model architectures
- The progression of resource-efficient AI
- The significance of lightweight models in enterprise environments
Understanding Nano Banana
- Core features and foundational design principles
- Defining model capabilities and inherent limitations
- Distinguishing Nano Banana from conventional LLMs
Deployment Models and Use Scenarios
- Benefits of on-device execution
- Comparing local versus cloud-based inference
- Choosing the optimal deployment strategy
Practical Applications Across Industries
- Enhancing internal automation and knowledge support
- Implementing customer-facing AI solutions
- Addressing operational and compliance-related needs
Integration Fundamentals
- Assessing system requirements
- Considerations for workflow and process optimization
- Introduction to APIs and toolchains
Cost Optimization and Efficiency
- Leveraging compact models to lower inference costs
- Striking a balance between performance and resource usage
- Planning for scalable implementation
Governance, Privacy, and Risk Management
- Safeguarding secure on-device operations
- Defining data boundaries and protective measures
- Aligning with enterprise policies and industry standards
Preparing for Organizational Adoption
- Developing internal skills and readiness
- Evaluating business impact through pilot initiatives
- Establishing the foundation for wider deployment
Summary and Next Steps
Requirements
- A solid grasp of general IT concepts
- Proficiency with fundamental software tools
- Experience with data-centric business workflows
Target Audience
- IT teams looking to integrate AI capabilities
- Business professionals seeking practical AI applications
- Technology leaders evaluating on-device LLM strategies
Testimonials (1)
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