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Duration 14 hours
Course Outline
Introduction to On-Device AI with Nano Banana
- Foundational principles of on-device inference.
- Architecture and capabilities of the Nano Banana model.
- Considerations for deployment on mobile platforms.
Setting Up Nano Banana and the Development Environment
- Installing Nano Banana SDK tools.
- Configuring build environments for Android and iOS.
- Managing dependencies and ensuring version compatibility.
Executing Nano Banana Models on Mobile Devices
- Loading and running prebuilt models.
- Navigating memory and compute limitations of mobile hardware.
- Strategies for real-time inference.
Developing AI Features with Nano Banana
- Incorporating 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 for thermal management, battery life, and resource usage.
Security and Privacy in On-Device AI
- Handling local data and adhering to compliance standards.
- Protecting models and ensuring secure execution.
- Identifying risks and implementing mitigation strategies.
Advanced Deployment Patterns
- Integrating hybrid on-device and cloud workflows.
- 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 backward compatibility.
Summary and Next Steps
Requirements
- A solid grasp of mobile application development principles.
- Proficiency in Python, Kotlin, or Swift.
- A working knowledge of machine learning concepts.
Audience
- Mobile developers.
- AI engineers.
- Technical professionals investigating on-device AI deployment.
Testimonials (1)
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