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Course Outline
Introduction to On-Device AI with Nano Banana
- Foundational principles of on-device inference
- Overview of Nano Banana model architecture and capabilities
- Key considerations for deployment on mobile platforms
Setting Up Nano Banana and the Development Environment
- Installation of Nano Banana SDK tools
- Configuration of Android and iOS build environments
- Management of dependencies and version compatibility
Executing Nano Banana Models on Mobile Devices
- Loading and running pre-built models
- Addressing memory and compute constraints on mobile hardware
- Strategies for real-time inference
Developing AI Features with Nano Banana
- Integrating text generation functionalities
- Implementing workflows for image generation and editing
- Combining multimodal inputs within applications
Performance Optimization and Benchmarking
- Profiling latency and throughput
- Techniques for quantization, pruning, and model compression
- Optimizing thermal output, battery consumption, and resource usage
Security and Privacy in On-Device AI
- Guidelines for local data handling and regulatory compliance
- Ensuring model protection and secure execution
- Identifying risks and implementing mitigation strategies
Advanced Deployment Patterns
- Hybrid workflows combining on-device and cloud processing
- Managing offline-first AI applications
- Scaling solutions for large user bases
Testing, Debugging, and Continuous Improvement
- Implementing CI/CD pipelines for AI-enabled mobile apps
- Conducting unit, integration, and performance testing
- Managing iterative model updates and ensuring backward compatibility
Summary and Next Steps
Requirements
- Understanding of mobile application development principles
- Experience with Python, Kotlin, or Swift
- Familiarity with core machine learning concepts
Target Audience
- Mobile developers
- AI engineers
- Technical professionals investigating on-device AI deployment
14 Hours
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