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Course Outline

Introduction to On-Device AI with Nano Banana

  • Fundamentals of on-device inference
  • Overview of Nano Banana model architecture and capabilities
  • Key deployment factors for mobile platforms

Setting Up Nano Banana and the Development Environment

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

Executing Nano Banana Models on Mobile Devices

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

Creating AI Features with Nano Banana

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

Performance Optimization and Benchmarking

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

Security and Privacy in On-Device AI

  • Local data handling and compliance requirements
  • Model protection and secure execution methods
  • Identifying risks and implementing mitigation strategies

Advanced Deployment Patterns

  • Implementing hybrid on-device and cloud workflows
  • Managing offline-first AI applications
  • Scaling solutions for extensive user bases

Testing, Debugging, and Continuous Improvement

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

Conclusion and Next Steps

Requirements

  • Proficiency in mobile application development
  • Proficiency in Python, Kotlin, or Swift
  • 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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