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Course Outline

Introduction to Edge and Agentic AI

  • Fundamental concepts of agentic artificial intelligence and edge computing infrastructures
  • Key operational factors including latency reduction, data privacy, and bandwidth utilization
  • Comparative analysis of architectural models: cloud-based versus edge-deployed agents

Designing Lightweight Agent Architectures

  • Decomposition of the agent operational loop to accommodate resource-constrained systems
  • Implementation of asynchronous frameworks to enhance computational efficiency
  • Strategies for maintaining equilibrium between autonomous functionality and network connectivity

Establishing the Development Environment

  • Deployment of Python-based frameworks tailored for edge artificial intelligence
  • Configuration of TensorFlow Lite and PyTorch Mobile environments
  • Installation of test infrastructure on Raspberry Pi devices or equivalent hardware platforms

Implementing On-Device Inference

  • Model conversion and quantization processes optimized for edge deployment
  • Execution of inference operations utilizing TensorFlow Lite and ONNX Runtime
  • Integration of inference outputs into agent decision-making workflows

Integrating Agents with Hardware and IoT Systems

  • Connection protocols for sensors, actuators, and Internet of Things (IoT) modules
  • Localized data acquisition and processing pipelines
  • Protocols for offline operation and event-driven behaviors

Optimization and Monitoring

  • Performance tuning to ensure low power consumption and high-speed execution
  • Application of edge caching mechanisms and model compression techniques
  • Procedures for monitoring and debugging edge-based agents

Practical Application: Deploying a Lightweight Agent on Edge Hardware

  • Development of a minimal autonomous agent designed for IoT or robotics applications
  • Execution of local model inference and logic implementation
  • Validation procedures to optimize latency and system reliability

Summary and Next Steps

Requirements

  • Demonstrated proficiency in Python scripting and software development
  • Foundational knowledge of machine learning implementation processes
  • Working understanding of embedded systems and edge computing architectures

Audience

  • Embedded software engineers tasked with integrating artificial intelligence capabilities into hardware platforms
  • Machine learning specialists focused on developing inference solutions for edge devices
  • Robotics engineering units deploying autonomous agents for mission-critical operations, ensuring compliance and efficacy for government applications.
 21 Hours

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