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Course Outline
Introduction to Nano Banana
- Overview of the framework and its core capabilities.
- Understanding the architecture and processing pipeline.
- Comparing Nano Banana with alternative on-device AI solutions.
Setting Up the Development Environment
- Preparing Android Studio for AI workloads.
- Integrating the Nano Banana SDK.
- Project configuration and dependency management.
Working with Nano Banana APIs
- Exploring core API methods.
- Loading and managing lightweight models.
- Executing inference tasks in real time.
Optimizing AI Performance on Android
- Strategies for achieving low-latency inference.
- Techniques for memory and resource management.
- Benchmarking approaches and optimization tools.
Designing AI-Driven User Experiences
- Implementing responsive UI interactions.
- Handling asynchronous tasks and callbacks.
- Aligning AI behaviors with Android UX guidelines.
Security and Privacy in On-Device AI
- Ensuring secure handling of user data.
- Techniques for privacy-preserving inference.
- Compliance considerations for enterprise deployments.
Deploying and Maintaining AI Features
- Packaging and publishing applications with embedded AI.
- Versioning and updating local models.
- Monitoring and improving performance post-deployment.
Advanced Use Cases and Integrations
- Combining Nano Banana with existing Android ML tools.
- Implementing multimodal AI features.
- Extending applications with custom lightweight models.
Summary and Next Steps
Requirements
- A solid grasp of Android application fundamentals.
- Proficiency in 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 on Android.
14 Hours
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