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Course Outline

Introduction to Nano Banana

  • Review of the framework’s key features and capabilities
  • Deep dive into the architecture and processing pipeline
  • Comparison of Nano Banana against other on-device AI alternatives

Establishing the Development Environment

  • Configuring Android Studio for AI-centric workloads
  • Embedding the Nano Banana SDK
  • Managing project configurations and dependencies

Utilizing Nano Banana APIs

  • Investigating essential API methods
  • Loading and orchestrating lightweight models
  • Running inference tasks in real time

Enhancing AI Performance on Android

  • Tactics for achieving low-latency inference
  • Methods for effective memory and resource management
  • Strategies for benchmarking and using optimization tools

Crafting AI-Driven User Experiences

  • Creating responsive UI interactions
  • Managing asynchronous tasks and callbacks
  • Ensuring AI behaviors align with Android UX standards

Security and Privacy in On-Device AI

  • Safeguarding the secure handling of user data
  • Methods for privacy-preserving inference
  • Compliance factors relevant to enterprise rollouts

Deployment and Ongoing Maintenance of AI Features

  • Packaging and releasing apps with embedded AI
  • Managing versioning and updates for local models
  • Monitoring and refining performance after deployment

Advanced Use Cases and Integrations

  • Merging Nano Banana with existing Android ML toolkits
  • Developing multimodal AI capabilities
  • Expanding app functionality with custom lightweight models

Summary and Next Steps

Requirements

  • Familiarity with Android application fundamentals
  • Proficiency in Kotlin or Java
  • Basic knowledge of mobile app debugging processes

Target Audience

  • Android developers creating AI-enhanced applications
  • Software engineers investigating on-device ML workflows
  • Technical teams assessing lightweight AI deployment on Android
 14 Hours

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