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Duration 14 hours
Course Outline
Introduction to On-Device AI with Nano Banana
- Core principles of on-device inference.
- Architecture and capabilities of the Nano Banana model.
- Deployment considerations for mobile platforms.
Nano Banana Setup and 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 prebuilt models.
- Navigating memory and compute constraints on mobile hardware.
- Strategies for real-time inference.
Creating AI Features with Nano Banana
- Integration of text generation functionalities.
- Implementation of image generation and editing workflows.
- Combining multimodal inputs within applications.
Performance Optimisation and Benchmarking
- Profiling latency and throughput.
- Techniques for quantisation, pruning, and model compression.
- Optimisation of thermal performance, battery life, and resource usage.
Security and Privacy in On-Device AI
- Local data handling and compliance considerations.
- Model protection and secure execution methods.
- Identification of risks and mitigation strategies.
Advanced Deployment Patterns
- Hybrid workflows combining on-device and cloud operations.
- Management of offline-first AI applications.
- Scalability strategies for large user bases.
Testing, Debugging, and Continuous Improvement
- Implementing CI/CD for AI-enabled mobile apps.
- Unit, integration, and performance testing.
- Iterative model updates and maintaining backward compatibility.
Summary and Next Steps
Requirements
- A solid understanding of mobile application development.
- Practical experience with Python, Kotlin, or Swift.
- Familiarity with core machine learning concepts.
Target Audience
- Mobile developers.
- AI engineers.
- Technical professionals exploring on-device AI deployment.
Testimonials (1)
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