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Course Outline
Introduction to Nano Banana
- Overview of the framework and its capabilities
- Understanding the architecture and processing pipeline
- Comparing Nano Banana with other 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 low-latency inference
- Memory and resource management techniques
- 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
- An understanding of Android application fundamentals
- Experience with Kotlin or Java
- Basic familiarity with mobile app debugging workflows
Audience
- Android developers building AI-enhanced apps
- Software engineers exploring on-device ML workflows
- Technical teams evaluating lightweight AI deployment on Android
14 Hours
Testimonials (1)
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