Course Outline

Introduction to Edge and Agentic AI

  • Overview of agentic AI and edge computing for government applications
  • Latency, privacy, and bandwidth considerations in public sector environments
  • Architectural comparison: cloud vs. edge agents for government use cases

Designing Lightweight Agent Architectures for Government

  • Breaking down the agent loop for constrained systems in public sector workflows
  • Asynchronous design for efficient computation to enhance governmental operations
  • Balancing autonomy and connectivity to support government governance and accountability

Setting Up the Development Environment for Government

  • Installing Python frameworks for edge AI in public sector projects
  • Configuring TensorFlow Lite and PyTorch Mobile for government applications
  • Deploying test environments on Raspberry Pi or similar devices for government testing

Implementing On-Device Inference for Government

  • Converting and quantizing models for edge deployment in public sector settings
  • Running inference with TensorFlow Lite and ONNX Runtime to support government operations
  • Integrating inference results into agent decision loops for enhanced governmental processes

Integrating Agents with Hardware and IoT for Government

  • Connecting sensors, actuators, and IoT modules in government systems
  • Local data collection and processing pipelines to support public sector workflows
  • Offline operation and event-triggered behavior for reliable government applications

Optimization and Monitoring for Government

  • Performance tuning for low power and high speed in governmental contexts
  • Edge caching and model compression techniques to enhance public sector efficiency
  • Monitoring and debugging edge agents to ensure reliable government operations

Hands-on Project: Deploying a Lightweight Agent on Edge Hardware for Government

  • Designing a small autonomous agent for an IoT or robotics task in public sector applications
  • Implementing model inference and local logic to support government tasks
  • Testing and optimizing for latency and reliability in governmental settings

Summary and Next Steps for Government

Requirements

  • Experience with Python programming for government applications
  • Basic understanding of machine learning workflows and their application in the public sector
  • Familiarity with embedded or edge computing concepts, particularly as they relate to government technology solutions

Audience

  • Embedded developers integrating AI into hardware systems for government use
  • Edge ML engineers designing on-device inference solutions for government projects
  • Robotics teams deploying agentic AI for autonomous operation in government settings
 21 Hours

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