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Course Outline

Introduction to Edge and Agentic AI

  • Overview of agentic AI and edge computing
  • Considerations regarding latency, privacy, and bandwidth
  • Comparative analysis of cloud versus edge agent architectures

Designing Lightweight Agent Architectures

  • Deconstructing the agent loop for constrained systems
  • Implementing asynchronous design for efficient computation
  • Balancing autonomy with connectivity requirements

Setting Up the Development Environment

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

Implementing On-Device Inference

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

Integrating Agents with Hardware and IoT

  • Connecting sensors, actuators, and IoT modules
  • Establishing local data collection and processing pipelines
  • Enabling offline operation and event-triggered behaviors

Optimization and Monitoring

  • Performance tuning to achieve low power consumption and high speed
  • Employing edge caching and model compression techniques
  • Monitoring and debugging edge agents

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

  • Designing a compact autonomous agent for IoT or robotics tasks
  • Implementing model inference and local logic
  • Testing and optimizing for latency and reliability

Summary and Next Steps

Requirements

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

Audience

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

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