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Duration 7 hours
Course Outline
Introduction to Compact LLMs
- Comprehending streamlined model architectures
- The progression of resource-efficient AI
- The significance of lightweight models for enterprises
Getting to Know Nano Banana
- Essential features and design foundations
- Model strengths and constraints
- Distinguishing Nano Banana from conventional LLMs
Deployment Strategies and Application Scenarios
- Benefits of on-device processing
- Comparing local and cloud-based inference
- Choosing the optimal deployment route
Real-World Applications Across Sectors
- Internal automation and knowledge support
- Client-facing interaction scenarios
- Operational and compliance-focused use cases
Foundations of Integration
- Reviewing system prerequisites
- Workflow and procedural factors
- Introduction to APIs and development toolchains
Efficiency and Cost Management
- Lowering inference expenses through compact models
- Optimizing the balance between performance and resources
- Preparing for scalable implementations
Governance, Confidentiality, and Risk Oversight
- Safeguarding secure on-device operations
- Comprehending data limits and protective measures
- Aligning with corporate policies and regulatory standards
Readiness for Enterprise Implementation
- Developing internal competencies and preparedness
- Measuring business impact via pilot initiatives
- Establishing the basis for wider expansion
Recap and Future Actions
Requirements
- A solid grasp of general IT principles
- Proficiency with fundamental software applications
- Knowledge of data-centric business processes
Target Audience
- IT teams integrating AI capabilities
- Business personnel interested in practical AI implementations
- Technology leaders assessing on-device LLM strategies
Testimonials (1)
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