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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 the enterprise context
Insights into Nano Banana
- Core features and architectural design principles
- Analyzing model strengths and constraints
- Distinguishing Nano Banana from conventional LLMs
Deployment Strategies and Application Scenarios
- On-device processing and its operational advantages
- Comparing local versus cloud-based inference
- Choosing the optimal deployment approach
Real-World Industry Applications
- Streamlining internal automation and knowledge support
- Implementing customer-facing AI solutions
- Addressing operational and compliance-related needs
Integration Essentials
- Reviewing necessary system requirements
- Considering workflow and process impacts
- Introduction to APIs and supporting toolchains
Optimizing Costs and Efficiency
- Lowering inference expenses through compact models
- Striking a balance between performance and resource usage
- Planning for scalable implementation
Governance, Data Privacy, and Risk Mitigation
- Securing on-device execution environments
- Comprehending data boundaries and protective measures
- Ensuring alignment with corporate policies and regulatory standards
Readying for Organizational Integration
- Developing internal skills and operational readiness
- Evaluating business impact via pilot initiatives
- Establishing the basis for wider adoption
Recap and Future Actions
Requirements
- Proficiency in general IT fundamentals
- Experience utilizing basic software applications
- Awareness of data-centric business processes
Target Audience
- IT teams integrating AI functionalities into their operations
- Business professionals interested in the practical application of AI
- Technology leaders assessing on-device LLM strategies
Testimonials (1)
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