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Duration 14 hours
Course Outline
Foundations of On-Device AI with Nano Banana
- Essential principles of local inference
- Nano Banana model structure and core capabilities
- Strategic considerations for mobile platform deployment
Nano Banana Configuration and Development Environment
- Setting up Nano Banana SDK tools
- Establishing Android and iOS build configurations
- Managing dependencies and ensuring version compatibility
Executing Nano Banana Models on Mobile Hardware
- Loading and running pre-compiled models
- Navigating memory and computational limits on mobile devices
- Strategies for real-time inference
Creating AI Features with Nano Banana
- Integrating text generation capabilities
- Building workflows for image creation and editing
- Handling combined multimodal inputs within applications
Performance Enhancement and Benchmarking
- Analyzing latency and throughput
- Applying quantization, pruning, and model compression
- Optimizing thermal, battery, and resource consumption
Security and Privacy in Local AI
- Managing local data and compliance requirements
- Ensuring model protection and secure execution
- Identifying risks and implementing mitigation tactics
Advanced Deployment Strategies
- Designing hybrid on-device and cloud workflows
- Managing offline-first AI applications
- Scaling solutions for extensive user bases
Testing, Debugging, and Continuous Improvement
- Implementing CI/CD for AI-enabled mobile apps
- Conducting unit, integration, and performance tests
- Managing iterative model updates and backward compatibility
Conclusion and Future Directions
Requirements
- Solid grasp of mobile application development
- Proficiency in Python, Kotlin, or Swift
- Knowledge of machine learning fundamentals
Intended Audience
- Mobile developers
- AI engineers
- Technical professionals exploring local AI deployment strategies
Testimonials (1)
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