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 Duration 14 hours

Course Outline

Foundations of On-Device AI with Nano Banana

  • Essential concepts of on-device inference.
  • Overview of the Nano Banana model architecture and its features.
  • Key considerations for deploying on mobile platforms.

Configuring Nano Banana and Development Environments

  • Setting up Nano Banana SDK tools.
  • Configuring build environments for Android and iOS.
  • Handling dependencies and ensuring version compatibility.

Executing Nano Banana Models on Mobile Hardware

  • Loading and running pre-constructed models.
  • Navigating memory and computational limits on mobile devices.
  • Strategies for achieving real-time inference.

Creating AI Features with Nano Banana

  • Incorporating text generation capabilities.
  • Developing workflows for image generation and editing.
  • Fusing multimodal inputs within applications.

Enhancing Performance and Conducting Benchmarks

  • Analyzing latency and throughput profiles.
  • Applying quantization, pruning, and model compression methods.
  • Optimizing for thermal management, battery life, and resource usage.

Ensuring Security and Privacy in On-Device AI

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

Advanced Deployment Strategies

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

Testing, Debugging, and Ongoing Refinement

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

Conclusion and Future Directions

Requirements

  • Solid knowledge of mobile application development principles.
  • Proficiency in Python, Kotlin, or Swift.
  • Working familiarity with core machine learning concepts.

Target Audience

  • Mobile software developers.
  • AI engineers.
  • Technical professionals interested in on-device AI deployment.

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