Course Outline

Introduction to On-Device AI for Government with Nano Banana

  • Core principles of on-device inference for government applications
  • Nano Banana model architecture and capabilities tailored for public sector use
  • Deployment considerations for mobile platforms in government environments

Nano Banana Setup and Development Environment for Government

  • Installing Nano Banana SDK tools for government systems
  • Configuring Android and iOS build environments for secure government operations
  • Managing dependencies and version compatibility in government IT infrastructures

Running Nano Banana Models on Mobile Devices for Government Use

  • Loading and executing prebuilt models in a secure, government-compliant manner
  • Addressing memory and compute constraints on mobile hardware used by government agencies
  • Implementing real-time inference strategies for efficient public sector operations

Building AI Features with Nano Banana for Government Applications

  • Integrating text generation functionalities to support government communications and documentation
  • Implementing image generation and editing workflows for public sector use cases
  • Combining multimodal inputs in apps to enhance user experience and operational efficiency for government services

Performance Optimization and Benchmarking for Government AI Solutions

  • Latency and throughput profiling to ensure responsive government applications
  • Quantization, pruning, and model compression techniques to optimize performance in resource-constrained environments
  • Thermal, battery, and resource usage optimization for sustainable public sector operations

Security and Privacy in On-Device AI for Government

  • Local data handling and compliance considerations to meet government regulations
  • Model protection and secure execution to safeguard sensitive information
  • Risk assessment and mitigation strategies to ensure robust security in government applications

Advanced Deployment Patterns for Government AI Applications

  • Hybrid on-device and cloud workflows to balance local processing with centralized data management
  • Managing offline-first AI applications to support government operations in areas with limited connectivity
  • Scaling for large user bases to accommodate the needs of extensive public sector networks

Testing, Debugging, and Continuous Improvement for Government AI Projects

  • CI/CD pipelines for continuous integration and deployment of AI-enabled mobile apps in government settings
  • Unit, integration, and performance testing to ensure reliability and efficiency of government applications
  • Iterative model updates and backward compatibility to maintain consistent service delivery in the public sector

Summary and Next Steps for Government AI Initiatives

Requirements

  • A comprehensive understanding of mobile application development for government use
  • Practical experience with programming languages such as Python, Kotlin, or Swift
  • Familiarity with fundamental machine learning concepts and techniques

Audience

  • Mobile developers for government projects
  • AI engineers working in the public sector
  • Technical professionals interested in deploying on-device AI solutions for government applications
 14 Hours

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