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Duration 14 hours
Course Outline
Introduction to Nano Banana
- Framework overview and capability assessment
- Analysis of architecture and processing pipelines
- Comparison with alternative on-device AI solutions
Establishing the Development Environment
- Configuring Android Studio for AI-centric tasks
- Embedding the Nano Banana SDK
- Managing project settings and dependencies
Utilizing Nano Banana APIs
- Exploration of essential API methods
- Loading and administration of lightweight models
- Real-time execution of inference tasks
Enhancing AI Performance on Android
- Tactics for achieving low-latency inference
- Methods for managing memory and resources
- Approaches to benchmarking and optimization tooling
Creating AI-Enhanced User Experiences
- Building responsive UI interactions
- Managing asynchronous operations and callbacks
- Aligning AI functionality with Android UX standards
Security and Privacy in On-Device AI
- Safeguarding user data handling
- Methods for privacy-centric inference
- Addressing compliance for enterprise implementations
Deployment and Maintenance of AI Features
- Packaging and releasing apps with integrated AI
- Managing local model versions and updates
- Post-deployment performance monitoring and refinement
Advanced Applications and Integrations
- Synergy between Nano Banana and existing Android ML tools
- Development of multimodal AI features
- Application extension using custom lightweight models
Recap and Path Forward
Requirements
- Foundation in Android application development
- Proficiency in Kotlin or Java
- Familiarity with basic mobile debugging processes
Target Audience
- Android developers creating AI-powered applications
- Software engineers investigating on-device ML workflows
- Technical teams assessing lightweight AI deployment on Android
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