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Course Outline

Fundamentals of On-Device AI using Nano Banana

  • Essential concepts of on-device inference
  • Nano Banana model structure and features
  • Key deployment factors for mobile ecosystems

Setting Up Nano Banana and the Development Environment

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

Executing Nano Banana Models on Mobile Hardware

  • Loading and running pre-built models
  • Addressing memory and computational limits on mobile devices
  • Techniques for real-time inference

Developing AI Features with Nano Banana

  • Adding text generation capabilities
  • Creating workflows for image generation and modification
  • Utilizing multimodal inputs within applications

Optimizing Performance and Conducting Benchmarks

  • Monitoring latency and throughput
  • Applying quantization, pruning, and model compression methods
  • Optimizing thermal management, battery life, and resource utilization

Ensuring Security and Privacy in On-Device AI

  • Local data management and regulatory compliance
  • Protecting models and ensuring secure execution
  • Identifying risks and applying mitigation strategies

Advanced Deployment Strategies

  • Combining on-device and cloud-based workflows
  • Managing offline-first AI application logic
  • Scaling solutions for extensive user bases

Testing, Debugging, and Continuous Refinement

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

Conclusion and Recommended Next Steps

Requirements

  • Knowledge of mobile application development
  • Proficiency in Python, Kotlin, or Swift
  • Basic understanding of machine learning principles

Target Audience

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

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