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Course Outline

Foundations of On-Device AI with Nano Banana

  • Essential concepts of on-device inference.
  • Overview of Nano Banana’s architecture and features.
  • Key considerations for mobile platform deployment.

Nano Banana Setup and 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-configured models.
  • Navigating memory and processing limits on mobile hardware.
  • Strategies for real-time inference.

Creating AI-Driven Features with Nano Banana

  • Integrating text generation capabilities.
  • Building workflows for image creation and editing.
  • Utilizing multimodal inputs within applications.

Performance Optimization and Benchmarking

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

Security and Privacy in On-Device AI

  • Considerations for local data management and regulatory compliance.
  • Safeguarding models and ensuring secure execution.
  • Identifying risks and implementing mitigation strategies.

Advanced Deployment Strategies

  • Designing hybrid workflows combining on-device and cloud resources.
  • Managing offline-first AI applications.
  • Scaling solutions for extensive user bases.

Testing, Debugging, and Continuous Improvement

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

Recap and Future Directions

Requirements

  • Fundamental knowledge of mobile app development.
  • Proficiency in Python, Kotlin, or Swift.
  • Basic familiarity with machine learning principles.

Target Audience

  • Mobile developers.
  • AI engineers.
  • Technical specialists investigating on-device AI implementation.
 14 Hours

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