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Course Outline
Introduction to On-Device AI with Nano Banana
- Fundamentals of on-device inference
- Nano Banana model architecture and features
- Deployment considerations for mobile platforms
Setting Up Nano Banana and the Development Environment
- Installing Nano Banana SDK tools
- Configuring Android and iOS build environments
- Managing dependencies and version compatibility
Executing Nano Banana Models on Mobile Devices
- Loading and running pre-built models
- Memory and compute limitations on mobile hardware
- Strategies for real-time inference
Creating AI Features with Nano Banana
- Integrating text generation capabilities
- Implementing image generation and editing workflows
- Combining multimodal inputs in applications
Performance Optimisation and Benchmarking
- Profiling latency and throughput
- Quantisation, pruning, and model compression techniques
- Optimising thermal, battery, and resource usage
Security and Privacy in On-Device AI
- Local data handling and compliance considerations
- Model protection and secure execution
- Identifying risks and mitigation strategies
Advanced Deployment Patterns
- Hybrid on-device and cloud workflows
- Managing offline-first AI applications
- Scaling for large user bases
Testing, Debugging, and Continuous Improvement
- CI/CD pipelines for AI-enabled mobile apps
- Unit, integration, and performance testing
- Iterative model updates and backward compatibility
Summary and Next Steps
Requirements
- A solid grasp of mobile application development
- Proficiency in Python, Kotlin, or Swift
- A working knowledge of machine learning principles
Target Audience
- Mobile developers
- AI engineers
- Technical professionals investigating on-device AI deployment
14 Hours
Testimonials (1)
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