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Course Outline

Foundations of On-Device AI with Nano Banana

  • Key principles of local inference
  • Overview of Nano Banana model architecture and features
  • Considerations for deploying on mobile platforms

Nano Banana Configuration and Development Environment

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

Executing Nano Banana Models on Mobile Devices

  • Loading and running pre-built models
  • Addressing memory and processing limits on mobile hardware
  • Strategies for real-time inference

Creating AI Features with Nano Banana

  • Incorporating text generation capabilities
  • Developing image generation and editing workflows
  • Utilizing multimodal inputs within applications

Optimizing Performance and Benchmarking

  • Profiling latency and data throughput
  • Applying quantization, pruning, and model compression
  • Optimizing for thermal management, battery life, and resource usage

Security and Privacy in On-Device AI

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

Advanced Deployment Strategies

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

Testing, Debugging, and Continuous Improvement

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

Conclusion and Future Steps

Requirements

  • A solid grasp of mobile application development principles
  • Proficiency in Python, Kotlin, or Swift
  • Basic familiarity with machine learning concepts

Target Audience

  • Mobile developers
  • AI engineers
  • Technical professionals interested in on-device AI deployment strategies
 14 Hours

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