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Duration 7 hours
Course Outline
Introduction to Lightweight LLMs
- Exploring compact model architectures
- Tracing the development of resource-efficient AI
- Understanding the significance of lightweight models for enterprises
Gaining Insight into Nano Banana
- Reviewing key features and design philosophies
- Examining model capabilities and inherent limitations
- Comparing Nano Banana with traditional LLMs
Deployment Models and Application Scenarios
- Leveraging on-device execution and its advantages
- Contrasting local versus cloud-based inference
- Choosing the optimal deployment strategy
Practical Applications Across Various Industries
- Implementing internal automation and knowledge support
- Developing customer-facing solutions
- Addressing operational and compliance-driven needs
Integration Essentials
- Assessing system requirements
- Considering workflow and process implications
- Introducing API and toolchain components
Cost Optimization and Efficiency
- Minimizing inference expenses through compact models
- Striking a balance between performance and resource usage
- Planning for scalable deployment strategies
Governance, Privacy, and Risk Management
- Safeguarding secure on-device operations
- Comprehending data boundaries and protective measures
- Ensuring alignment with enterprise policies and standards
Preparing for Organizational Adoption
- Developing internal capabilities and readiness
- Evaluating business impact through pilot projects
- Establishing the foundation for wider implementation
Summary and Path Forward
Requirements
- A foundational understanding of general IT concepts
- Hands-on experience with basic software tools
- Knowledge of data-driven business processes
Intended Audience
- General IT teams looking to integrate AI capabilities
- Business professionals interested in applying AI practically
- Technology managers assessing on-device LLM strategies
Testimonials (1)
Flow , vibe and topic on presentation