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Course Outline

Introduction to On-Device AI with Nano Banana

  • Fundamental principles of on-device inference
  • Overview of Nano Banana’s model architecture and core capabilities
  • Key deployment considerations for mobile platforms

Setting Up Nano Banana and the Development Environment

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

Executing Nano Banana Models on Mobile Devices

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

Developing AI Features with Nano Banana

  • Integrating text generation capabilities
  • Implementing workflows for image generation and editing
  • Combining multimodal inputs within applications

Performance Optimization and Benchmarking

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

Security and Privacy in On-Device AI

  • Local data handling and regulatory compliance considerations
  • Model protection and secure execution environments
  • Identifying risks and implementing mitigation strategies

Advanced Deployment Patterns

  • Designing hybrid workflows that leverage both on-device and cloud resources
  • 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 ensuring backward compatibility

Summary and Next Steps

Requirements

  • A solid understanding of mobile application development practices
  • Proficiency in Python, Kotlin, or Swift
  • A foundational grasp of machine learning concepts

Intended Audience

  • Mobile developers
  • AI engineers
  • Technical professionals exploring the deployment of on-device AI
 14 Hours

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