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 Duration 21 hours

Course Outline

Fundamentals of Edge and Agentic AI

  • Foundational concepts of agentic AI and edge computing
  • Evaluation of latency, data privacy, and bandwidth constraints
  • Comparative analysis of cloud-based versus edge-based agent architectures

Development of Lightweight Agent Architectures

  • Decomposition of agent loops for resource-constrained systems
  • Asynchronous design patterns for computational efficiency
  • Strategic balance between autonomous operation and network connectivity

Development Environment Configuration

  • Installation of Python frameworks specific to edge AI
  • Configuration of TensorFlow Lite and PyTorch Mobile environments
  • Deployment of test environments on Raspberry Pi or equivalent hardware

Execution of On-Device Inference

  • Model conversion and quantization strategies for edge deployment
  • Implementation of inference via TensorFlow Lite and ONNX Runtime
  • Integration of inference outputs into agent decision-making cycles

Integration of Agents with Hardware and IoT Systems

  • Interface design for sensors, actuators, and IoT modules
  • Development of local data collection and processing pipelines
  • Establishment of offline operation and event-driven behavioral responses

Optimization and Performance Monitoring

  • Tuning for low power consumption and high operational speed
  • Application of edge caching and model compression techniques
  • Implementation of monitoring and debugging protocols for edge agents

Practical Application: Deploying a Lightweight Agent on Edge Hardware

  • Design of a compact autonomous agent for IoT or robotics tasks
  • Implementation of model inference and local logic modules
  • Validation and optimization for latency and system reliability

Conclusions and Future Directions

Requirements

  • Proficiency in Python programming
  • Fundamental knowledge of machine learning workflows
  • Working familiarity 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 deploying agentic AI for autonomous operational tasks

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