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Course Outline
Introduction to On-Device AI with Nano Banana
- Fundamental 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 build environments for Android and iOS.
- Managing dependencies and ensuring version compatibility.
Running Nano Banana Models on Mobile Devices
- Loading and executing pre-built models.
- Understanding memory and compute constraints on mobile hardware.
- Implementing real-time inference strategies.
Building 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.
- Applying quantization, pruning, and model compression techniques.
- Optimizing thermal output, battery consumption, and resource usage.
Security and Privacy in On-Device AI
- Local data handling and compliance considerations.
- 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
- CI/CD practices for AI-enabled mobile applications.
- Conducting unit, integration, and performance testing.
- Managing iterative model updates and ensuring backward compatibility.
Summary and Next Steps
Requirements
- A solid understanding of mobile application development.
- Experience with Python, Kotlin, or Swift.
- Familiarity with core machine learning concepts.
Target Audience
- Mobile developers.
- AI engineers.
- Technical professionals interested in on-device AI deployment.
14 Hours
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