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Duration 14 hours
Course Outline
Introduction to On-Device AI with Nano Banana
- Fundamentals of on-device inference
- Nano Banana model architecture and features
- Key considerations for mobile platform deployment
Nano Banana Setup and Development Environment
- Installing Nano Banana SDK tools
- Setting up Android and iOS build environments
- Handling dependencies and ensuring version compatibility
Running Nano Banana Models on Mobile Devices
- Loading and running pre-built models
- Navigating memory and compute limits on mobile hardware
- Strategies for real-time inference
Building AI Features with Nano Banana
- Incorporating text generation capabilities
- Creating image generation and editing workflows
- Integrating multimodal inputs within applications
Performance Optimization and Benchmarking
- Profiling latency and throughput
- Techniques for quantization, pruning, and model compression
- Optimizing thermal, battery, and resource usage
Security and Privacy in On-Device AI
- Local data management and compliance requirements
- Protecting models and ensuring secure execution
- Identifying risks and implementing mitigation strategies
Advanced Deployment Patterns
- Combining on-device and cloud workflows
- Overseeing offline-first AI applications
- Scaling solutions for large user bases
Testing, Debugging, and Continuous Improvement
- CI/CD practices for AI-enabled mobile apps
- Unit, integration, and performance testing
- Iterative model updates and maintaining backward compatibility
Summary and Next Steps
Requirements
- A solid grasp of mobile application development
- Proficiency in Python, Kotlin, or Swift
- Knowledge of fundamental machine learning concepts
Audience
- Mobile developers
- AI engineers
- Technical professionals investigating on-device AI deployment
Testimonials (1)
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