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 Duration 14 hours

Course Outline

Getting to Know Nano Banana

  • Key features and capabilities of the framework
  • Diving into the architecture and processing pipeline
  • Positioning Nano Banana against other on-device AI alternatives

Configuring the Development Environment

  • Setting up Android Studio for AI-focused workloads
  • Embedding the Nano Banana SDK
  • Handling project setup and dependency management

Leveraging Nano Banana APIs

  • Discovering essential API methods
  • Loading and overseeing lightweight models
  • Performing inference tasks with real-time efficiency

Enhancing AI Performance on Android

  • Techniques for achieving low-latency inference
  • Effective memory and resource management strategies
  • Utilizing benchmarking methods and optimization tools

Crafting AI-Powered User Experiences

  • Building responsive UI interactions
  • Managing asynchronous tasks and callbacks effectively
  • Aligning AI behaviors with Android UX standards

Safeguarding Security and Privacy in On-Device AI

  • Implementing secure handling of user data
  • Applying methods for privacy-preserving inference
  • Addressing compliance needs for enterprise rollouts

Release and Ongoing Maintenance of AI Features

  • Packaging and distributing apps with embedded AI
  • Managing version control and local model updates
  • Tracking and refining performance after deployment

Advanced Applications and Integrations

  • Merging Nano Banana with existing Android ML utilities
  • Developing multimodal AI functionalities
  • Expanding apps using custom lightweight models

Wrap-Up and Future Directions

Requirements

  • A solid grasp of Android application fundamentals
  • Proficiency in either 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

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