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Course Outline
Foundations of Lightweight LLMs
- Exploring compact model architectures
- The progression of resource-efficient AI
- The significance of lightweight models in enterprise settings
Overview of Nano Banana
- Essential features and design philosophy
- Model strengths and constraints
- Distinguishing Nano Banana from conventional LLMs
Deployment Strategies and Application Scenarios
- On-device processing and its advantages
- Comparing local and cloud-based inference
- Choosing the optimal deployment approach
Industry-Specific Practical Uses
- Internal process automation and knowledge support
- Applications directed at customer engagement
- Scenarios driven by operational needs and compliance
Core Integration Concepts
- Reviewing system prerequisites
- Considerations for workflows and processes
- Introduction to APIs and the toolchain
Cost Management and Efficiency
- Lowering inference expenses through compact models
- Striking a balance between performance and resource usage
- Strategizing for scalable implementations
Governance, Data Privacy, and Risk Oversight
- Safeguarding secure on-device operations
- Defining data boundaries and protective measures
- Aligning with corporate policies and industry standards
Facilitating Organizational Implementation
- Developing internal expertise and readiness
- Evaluating business impact via pilot initiatives
- Establishing the foundation for wider adoption
Recap and Future Actions
Requirements
- A solid grasp of general IT principles
- Proficiency with standard software tools
- Knowledge of data-centric business operations
Target Audience
- IT teams integrating AI functionalities
- Business professionals interested in applying AI in practice
- Technology leaders assessing on-device LLM strategies
7 Hours
Testimonials (1)
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