Plan du cours

Introduction to On-Device AI with Nano Banana

  • Core principles of on-device inference
  • Nano Banana model architecture and capabilities
  • Deployment considerations for mobile platforms

Nano Banana Setup and Development Environment

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

Running Nano Banana Models on Mobile Devices

  • Loading and executing prebuilt models
  • Memory and compute constraints on mobile hardware
  • Real-time inference strategies

Building AI Features with Nano Banana

  • Integrating text generation functionalities
  • Implementing image generation and editing workflows
  • Combining multimodal inputs in apps

Performance Optimization and Benchmarking

  • Latency and throughput profiling
  • Quantization, pruning, and model compression techniques
  • Thermal, battery, and resource usage optimization

Security and Privacy in On-Device AI

  • Local data handling and compliance considerations
  • Model protection and secure execution
  • Risks and mitigation strategies

Advanced Deployment Patterns

  • Hybrid on-device and cloud workflows
  • Managing offline-first AI applications
  • Scaling for large user bases

Testing, Debugging, and Continuous Improvement

  • CI/CD for AI-enabled mobile apps
  • Unit, integration, and performance testing
  • Iterative model updates and backward compatibility

Summary and Next Steps

Pré requis

  • An understanding of mobile application development
  • Experience with Python, Kotlin, or Swift
  • Familiarity with machine learning concepts

Audience

  • Mobile developers
  • AI engineers
  • Technical professionals exploring on-device AI deployment
 14 Heures

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