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Course Outline
Introduction to On-Device AI with Nano Banana
- Core principles of on-device inference
- Nano Banana model architecture and capabilities
- Deployment considerations for mobile platforms
Nano Banana Setup and Development Environment
- Installing Nano Banana SDK tools
- Configuring Android and iOS build environments
- Managing dependencies and version compatibility
Running Nano Banana Models on Mobile Devices
- Loading and executing prebuilt models
- Memory and compute constraints on mobile hardware
- Real-time inference strategies
Building AI Features with Nano Banana
- Integrating text generation functionalities
- Implementing image generation and editing workflows
- Combining multimodal inputs in apps
Performance Optimization and Benchmarking
- Latency and throughput profiling
- Quantization, pruning, and model compression techniques
- Thermal, battery, and resource usage optimization
Security and Privacy in On-Device AI
- Local data handling and compliance considerations
- Model protection and secure execution
- 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 for AI-enabled mobile apps
- Unit, integration, and performance testing
- Iterative model updates and backward compatibility
Summary and Next Steps
Requirements
- An understanding of mobile application development
- Experience with Python, Kotlin, or Swift
- Familiarity with machine learning concepts
Audience
- Mobile developers
- AI engineers
- Technical professionals exploring on-device AI deployment
14 Hours
Testimonials (1)
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