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 Duration 14 hours

Course Outline

Introduction to On-Device AI with Nano Banana

  • Fundamental principles of on-device inference
  • Nano Banana model architecture and functional capabilities
  • Deployment considerations specific to mobile platforms

Setting Up Nano Banana and the Development Environment

  • Installing Nano Banana SDK tools
  • Configuring Android and iOS build environments
  • Managing dependencies and ensuring version compatibility

Executing Nano Banana Models on Mobile Devices

  • Loading and running pre-built models
  • Navigating memory and compute constraints on mobile hardware
  • Strategies for real-time inference

Creating AI Features with Nano Banana

  • Incorporating text generation functionalities
  • Building workflows for image generation and editing
  • Integrating multimodal inputs within applications

Optimizing Performance and Benchmarking

  • Profiling latency and throughput
  • Techniques for quantization, pruning, and model compression
  • Optimizing thermal management, battery life, and resource consumption

Security and Privacy in On-Device AI

  • Local data handling and regulatory compliance
  • Model protection and secure execution environments
  • Identifying risks and implementing mitigation strategies

Advanced Deployment Patterns

  • Combining on-device and cloud workflows for hybrid solutions
  • Managing offline-first AI applications
  • Scaling solutions to support large user bases

Testing, Debugging, and Continuous Improvement

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

Summary and Next Steps

Requirements

  • A solid grasp of mobile application development
  • Hands-on experience with Python, Kotlin, or Swift
  • Familiarity with core machine learning concepts

Target Audience

  • Mobile developers
  • AI engineers
  • Technical professionals investigating on-device AI deployment

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