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

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