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Course Outline
Introduction to Edge and Agentic AI
- Overview of agentic AI and edge computing
- Key considerations for latency, privacy, and bandwidth
- Architectural comparison: cloud-based vs. edge-based agents
Designing Lightweight Agent Architectures
- Deconstructing the agent loop for constrained systems
- Asynchronous design strategies for efficient computation
- Balancing autonomy with connectivity
Setting Up the Development Environment
- Installing Python frameworks for edge AI
- Configuring TensorFlow Lite and PyTorch Mobile
- Deploying test environments on Raspberry Pi or comparable devices
Implementing On-Device Inference
- Model conversion and quantization for edge deployment
- Executing inference via TensorFlow Lite and ONNX Runtime
- Incorporating inference results into agent decision loops
Integrating Agents with Hardware and IoT
- Linking sensors, actuators, and IoT modules
- Local data collection and processing pipelines
- Offline operation and event-triggered behaviors
Optimization and Monitoring
- Performance tuning for low power consumption and high speed
- 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
- Proficiency in Python programming
- Fundamental understanding of machine learning workflows
- Basic knowledge of embedded or edge computing concepts
Target Audience
- Embedded developers integrating AI into hardware systems
- Edge ML engineers developing on-device inference solutions
- Robotics teams deploying agentic AI for autonomous operations
21 Hours
