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Course Outline

Introduction to On-Device AI using Nano Banana

  • Foundations of on-device inference
  • Overview of Nano Banana’s architecture and capabilities
  • Key considerations for deploying on mobile platforms

Setting Up Nano Banana and the Development Environment

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

Executing Nano Banana Models on Mobile Hardware

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

Creating AI Features with Nano Banana

  • Integrating text generation capabilities
  • Building workflows for image generation and editing
  • Handling combined multimodal inputs within apps

Performance Tuning and Benchmarking

  • Profiling latency and throughput
  • Applying quantization, pruning, and model compression
  • Optimising thermal output, battery life, and resource usage

Security and Privacy in On-Device AI

  • Managing local data and compliance requirements
  • Safeguarding models and ensuring secure execution
  • Identifying risks and implementing mitigation strategies

Advanced Deployment Patterns

  • 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 testing
  • Managing iterative model updates and maintaining backward compatibility

Summary and Next Steps

Requirements

  • A solid grasp of mobile application development principles
  • Proficiency in Python, Kotlin, or Swift
  • Basic knowledge of machine learning fundamentals

Target Audience

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

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