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Duration 14 hours
Course Outline
Introduction to On-Device AI with Nano Banana
- Fundamental principles of on-device inference
- Nano Banana model architecture and functional capabilities
- Deployment considerations specific to mobile platforms
Setting Up Nano Banana and the Development Environment
- Installing Nano Banana SDK tools
- Configuring Android and iOS build environments
- Managing dependencies and ensuring version compatibility
Executing Nano Banana Models on Mobile Devices
- Loading and running pre-built models
- Navigating memory and compute constraints on mobile hardware
- Strategies for real-time inference
Creating AI Features with Nano Banana
- Incorporating text generation functionalities
- Building workflows for image generation and editing
- Integrating multimodal inputs within applications
Optimizing Performance and Benchmarking
- Profiling latency and throughput
- Techniques for quantization, pruning, and model compression
- Optimizing thermal management, battery life, and resource consumption
Security and Privacy in On-Device AI
- Local data handling and regulatory compliance
- Model protection and secure execution environments
- Identifying risks and implementing mitigation strategies
Advanced Deployment Patterns
- Combining on-device and cloud workflows for hybrid solutions
- Managing offline-first AI applications
- Scaling solutions to support large user bases
Testing, Debugging, and Continuous Improvement
- Implementing CI/CD for AI-enabled mobile apps
- Conducting unit, integration, and performance testing
- Managing iterative model updates and maintaining backward compatibility
Summary and Next Steps
Requirements
- A solid grasp of mobile application development
- Hands-on 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
Testimonials (1)
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