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Duration 14 hours
Course Outline
Introduction to Nano Banana
- High-level view of the framework and its core capabilities
- Analysis of the architecture and processing pipeline
- Benchmarking Nano Banana against alternative on-device AI solutions
Configuring the Development Environment
- Configuring Android Studio for AI-specific workloads
- Integration of the Nano Banana SDK
- Project setup and dependency handling
Utilizing Nano Banana APIs
- Investigation of essential API methods
- Loading and managing lightweight models
- Executing inference tasks in real time
Enhancing AI Performance on Android
- Tactics for achieving low-latency inference
- Techniques for memory and resource management
- Approaches for benchmarking and optimization tools
Crafting AI-Driven User Experiences
- Building responsive UI interactions
- Managing asynchronous tasks and callbacks
- Aligning AI behaviors with Android UX standards
Security and Privacy in On-Device AI
- Securing the handling of user data
- Methods for privacy-preserving inference
- Compliance considerations for enterprise-grade deployments
Deployment and Maintenance of AI Features
- Packaging and releasing applications with embedded AI
- Managing versions and updates for local models
- Monitoring and enhancing performance after deployment
Advanced Use Cases and Integrations
- Integrating Nano Banana with existing Android ML tools
- Developing multimodal AI features
- Expanding applications with custom lightweight models
Recap and Future Directions
Requirements
- Fundamental knowledge of Android application development
- Proficiency in Kotlin or Java
- Basic command of mobile app debugging processes
Target Audience
- Android developers creating AI-empowered applications
- Software engineers investigating on-device machine learning workflows
- Technical teams assessing lightweight AI deployment strategies on Android
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