Course Outline

Introduction to Edge and Agentic AI

  • Overview of agentic AI and edge computing
  • Latency, privacy, and bandwidth considerations
  • Architectural comparison: cloud vs. edge agents

Designing Lightweight Agent Architectures

  • Breaking down the agent loop for constrained systems
  • Asynchronous design for efficient computation
  • Balancing autonomy and connectivity

Setting Up the Development Environment

  • Installing Python frameworks for edge AI
  • Configuring TensorFlow Lite and PyTorch Mobile
  • Deploying test environments on Raspberry Pi or similar devices

Implementing On-Device Inference

  • Converting and quantizing models for edge deployment
  • Running inference with TensorFlow Lite and ONNX Runtime
  • Integrating inference results into agent decision loops

Integrating Agents with Hardware and IoT

  • Connecting sensors, actuators, and IoT modules
  • Local data collection and processing pipelines
  • Offline operation and event-triggered behavior

Optimization and Monitoring

  • Performance tuning for low power and high speed
  • Edge caching and model compression techniques
  • Monitoring and debugging edge agents

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

  • Designing a small autonomous agent for an IoT or robotics task
  • Implementing model inference and local logic
  • Testing and optimizing for latency and reliability

Summary and Next Steps

Requirements

  • Experience with Python programming
  • Basic understanding of machine learning workflows
  • Familiarity with embedded or edge computing concepts

Audience

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

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