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Course Outline

Introduction to Edge and Agentic AI

  • Foundations of agentic AI and edge computing
  • Key factors: latency, privacy, and bandwidth
  • Comparing cloud-based versus edge-based agent architectures

Architecting Lightweight Agent Systems

  • Deconstructing the agent loop for constrained systems
  • Utilizing asynchronous design for computational efficiency
  • Striking a balance between autonomy and network connectivity

Configuring the Development Environment

  • Setting up Python frameworks for edge AI
  • Setting up TensorFlow Lite and PyTorch Mobile
  • Establishing test environments on Raspberry Pi or comparable hardware

Executing On-Device Inference

  • Model conversion and quantization for edge deployment
  • Running inference via TensorFlow Lite and ONNX Runtime
  • Incorporating inference outputs into agent decision-making cycles

Connecting Agents to Hardware and IoT

  • Linking sensors, actuators, and IoT modules
  • Building local data collection and processing pipelines
  • Enabling offline operation and event-driven behaviors

Performance Optimization and Monitoring

  • Tuning for low power consumption and high speed
  • Techniques for edge caching and model compression
  • Monitoring and debugging edge-based agents

Practical Project: Deploying a Lightweight Agent on Edge Hardware

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

Conclusion and Path Forward

Requirements

  • Proficiency in Python programming
  • Fundamental knowledge of machine learning workflows
  • Akquainted with embedded or edge computing principles

Target Audience

  • Embedded developers integrating AI capabilities into hardware systems
  • Edge ML engineers developing on-device inference solutions
  • Robotics teams implementing agentic AI for autonomous operations
 21 Hours

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