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Course Outline

Introduction to On-Device AI via Nano Banana

  • Fundamental principles of on-device inference
  • Overview of Nano Banana model architecture and features
  • Deployment considerations for mobile platforms

Nano Banana Configuration and Development Environment

  • Installation of Nano Banana SDK tools
  • Setup of Android and iOS build environments
  • Managing dependencies and ensuring version compatibility

Executing Nano Banana Models on Mobile Devices

  • Loading and running prebuilt models
  • Navigating memory and compute limitations on mobile hardware
  • Strategies for real-time inference

Creating AI Features with Nano Banana

  • Integration of text generation capabilities
  • Implementation of image generation and editing workflows
  • Combining multimodal inputs within applications

Performance Tuning and Benchmarking

  • Profiling latency and throughput
  • Application of quantization, pruning, and model compression techniques
  • Optimizing for thermal, battery, and resource usage

Security and Privacy in On-Device AI

  • Handling local data and compliance requirements
  • Model protection and secure execution methods
  • Identifying risks and implementing mitigation strategies

Advanced Deployment Patterns

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

Testing, Debugging, and Continuous Improvement

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

Wrap-up and Next Steps

Requirements

  • A solid grasp of mobile application development
  • Proficiency in Python, Kotlin, or Swift
  • A working knowledge of machine learning concepts

Target Audience

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

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