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Duration 14 hours
Course Outline
Introduction to Nano Banana
- Overview of the framework’s features and capabilities
- Insights into the architecture and processing pipeline
- Comparison of Nano Banana against other on-device AI solutions
Preparing the Development Environment
- Configuring Android Studio for AI workloads
- Incorporating the Nano Banana SDK
- Managing project configuration and dependencies
Utilizing Nano Banana APIs
- Exploring essential API methods
- Loading and managing lightweight models
- Performing inference tasks in real time
Enhancing AI Performance on Android
- Strategies for achieving low-latency inference
- Techniques for managing memory and resources
- Approaches to benchmarking and optimization tools
Crafting AI-Driven User Experiences
- Implementing responsive UI interactions
- Managing asynchronous tasks and callbacks
- Aligning AI behaviors with Android UX standards
Security and Privacy in On-Device AI
- Ensuring the secure handling of user data
- METHODS for privacy-preserving inference
- Compliance considerations for enterprise-level deployments
Deployment and Maintenance of AI Features
- Packaging and publishing applications with embedded AI
- Versioning and updating local models
- Monitoring and refining performance after deployment
Advanced Use Cases and Integrations
- Integrating Nano Banana with existing Android ML tools
- Implementing multimodal AI features
- Extending applications using custom lightweight models
Conclusion and Future Steps
Requirements
- Proficiency in Android application fundamentals
- Practical experience with Kotlin or Java
- Basic familiarity with mobile app debugging workflows
Target Audience
- Android developers creating AI-enhanced applications
- Software engineers investigating on-device machine learning workflows
- Technical teams assessing lightweight AI deployment strategies on Android
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