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Duration 14 hours
Course Outline
Introduction to Nano Banana
- Overview of the framework's key capabilities
- Understanding its architecture and processing pipeline
- Comparison with other on-device AI solutions
Configuring the Development Environment
- Preparing Android Studio for AI workloads
- Integrating the Nano Banana SDK
- Managing project configuration and dependencies
Utilizing Nano Banana APIs
- Exploring core API methods
- Loading and managing lightweight models
- Running real-time inference tasks
Optimizing AI Performance on Android
- Strategies for minimizing latency in inference
- Techniques for managing memory and resources
- Approaches for benchmarking and optimization tools
Crafting AI-Driven User Experiences
- Implementing responsive UI interactions
- Managing asynchronous tasks and callbacks
- Aligning AI behaviors with Android UX guidelines
Security and Privacy in On-Device AI
- Ensuring secure handling of user data
- Methods for privacy-preserving inference
- Compliance considerations for enterprise deployments
Deploying and Maintaining AI Features
- Packaging and publishing apps with embedded AI
- Versioning and updating local models
- Monitoring and enhancing performance after deployment
Advanced Use Cases and Integrations
- Combining Nano Banana with existing Android ML tools
- Implementing multimodal AI features
- Extending apps with custom lightweight models
Summary and Next Steps
Requirements
- A solid grasp of Android application fundamentals
- Proficiency in either Kotlin or Java
- Basic knowledge of mobile app debugging workflows
Target Audience
- Android developers creating AI-augmented applications
- Software engineers exploring on-device ML workflows
- Technical teams assessing lightweight AI deployment on Android
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