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Course Outline

Fundamentals of On-Device AI Using Nano Banana

  • Essential principles of local inference
  • Overview of Nano Banana’s architecture and feature set
  • Key deployment factors for mobile ecosystems

Setting Up the Nano Banana Development Environment

  • Installing Nano Banana SDK components
  • Configuring build environments for Android and iOS
  • Handling dependencies and ensuring version compatibility

Executing Nano Banana Models on Mobile Hardware

  • Loading and running pre-built models
  • Navigating memory and processing limits on mobile devices
  • Techniques for real-time inference

Developing AI Capabilities with Nano Banana

  • Embedding text generation features
  • Building workflows for image generation and editing
  • Leveraging multimodal inputs within applications

Optimizing Performance and Conducting Benchmarks

  • Analyzing latency and throughput
  • Applying quantization, pruning, and compression methods
  • Improving thermal management, battery life, and resource efficiency

Addressing Security and Privacy in On-Device AI

  • Managing local data and ensuring compliance
  • Safeguarding models and ensuring secure execution
  • Identifying risks and implementing mitigation strategies

Advanced Deployment Strategies

  • Combining on-device and cloud-based workflows
  • Handling offline-first AI application architectures
  • Scaling solutions for broad user bases

Testing, Debugging, and Continuous Iteration

  • Implementing CI/CD for AI-enabled mobile applications
  • Conducting unit, integration, and performance tests
  • Managing iterative model updates and maintaining backward compatibility

Recap and Future Directions

Requirements

  • Proficiency in mobile application development
  • Practical experience with Python, Kotlin, or Swift
  • A solid grasp of core machine learning principles

Target Audience

  • Mobile developers
  • AI engineers
  • Technical professionals exploring on-device AI integration
 14 Hours

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