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Duration 14 hours
Course Outline
Overview of On-Device AI with Nano Banana
- Fundamental concepts of on-device inference
- Structure and features of the Nano Banana model
- Key deployment factors for mobile ecosystems
Setting Up Nano Banana and the Development Environment
- Installing Nano Banana SDK utilities
- Setting up Android and iOS build configurations
- Handling dependencies and ensuring version compatibility
Executing Nano Banana Models on Mobile Hardware
- Initializing and running pre-compiled models
- Navigating memory and processing limits on mobile devices
- Strategies for real-time inference
Developing AI Capabilities with Nano Banana
- Embedding text generation features
- Establishing workflows for image creation and modification
- Merging multimodal data inputs within applications
Optimizing Performance and Conducting Benchmarks
- Analyzing latency and throughput
- Techniques for quantization, pruning, and model reduction
- Improving thermal management, battery life, and resource utilization
Ensuring Security and Privacy in On-Device AI
- Managing local data and adhering to compliance standards
- Safeguarding models and ensuring secure execution
- Identifying risks and applying mitigation tactics
Sophisticated Deployment Architectures
- Combining on-device and cloud-based processes
- Managing offline-priority AI applications
- Scalability strategies for extensive user bases
Testing, Troubleshooting, and Ongoing Refinement
- Implementing CI/CD for AI-enabled mobile applications
- Executing unit, integration, and performance tests
- Managing iterative model updates and maintaining backward compatibility
Conclusion and Future Directions
Requirements
- Knowledge of mobile application development practices
- Proficiency in Python, Kotlin, or Swift
- Understanding of core machine learning principles
Intended Audience
- Mobile developers
- AI engineers
- Technical specialists investigating on-device AI implementation
Testimonials (1)
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