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Course Outline
Introduction to Edge and Agentic AI
- Overview of agentic AI principles and edge computing
- Key considerations regarding latency, privacy, and bandwidth
- Architectural comparison: cloud-based versus edge-based agents
Designing Lightweight Agent Architectures
- Deconstructing the agent loop for constrained systems
- Employing asynchronous design for computational efficiency
- Achieving a balance between autonomy and connectivity
Setting Up the Development Environment
- Installing essential Python frameworks for edge AI
- Configuring TensorFlow Lite and PyTorch Mobile
- Deploying test environments on Raspberry Pi or equivalent devices
Implementing On-Device Inference
- Converting and quantizing models for edge deployment
- Executing inference via TensorFlow Lite and ONNX Runtime
- Integrating inference outputs into agent decision loops
Integrating Agents with Hardware and IoT
- Connecting sensors, actuators, and IoT modules
- Establishing local data collection and processing pipelines
- Managing offline operations and event-triggered behaviors
Optimization and Monitoring
- Tuning performance for low power consumption and high speed
- Applying edge caching and model compression techniques
- Monitoring and debugging edge agent performance
Hands-on Project: Deploying a Lightweight Agent on Edge Hardware
- Designing a compact autonomous agent for IoT or robotics applications
- Implementing model inference and local logic components
- Testing and optimizing for latency and reliability
Summary and Next Steps
Requirements
- Proficiency in Python programming
- Fundamental knowledge of machine learning workflows
- Basic familiarity with embedded or edge computing principles
Target Audience
- Embedded developers integrating AI capabilities into hardware systems
- Edge ML engineers crafting on-device inference solutions
- Robotics teams implementing agentic AI for autonomous functions
21 Hours
