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Course Outline
Introduction to Edge and Agentic AI
- Overview of agentic AI and edge computing
- Considerations for latency, privacy, and bandwidth
- Comparing architectures: cloud-based vs. edge-based agents
Designing Lightweight Agent Architectures
- Simplifying the agent loop for constrained systems
- Implementing asynchronous design for computational efficiency
- 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 equivalent devices
Implementing On-Device Inference
- Model conversion and quantization for edge deployment
- Executing inference with TensorFlow Lite and ONNX Runtime
- Incorporating inference outputs into agent decision loops
Integrating Agents with Hardware and IoT
- Linking sensors, actuators, and IoT modules
- Establishing local data collection and processing pipelines
- Supporting offline operation and event-driven behavior
Optimization and Monitoring
- Tuning performance for low power consumption and high speed
- Techniques for edge caching and model compression
- Monitoring and troubleshooting edge agents
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
- Testing and refining for latency and reliability
Summary and Next Steps
Requirements
- Proficiency in Python programming
- Fundamental grasp of machine learning workflows
- Awareness of embedded or edge computing principles
Target Audience
- Embedded developers integrating AI into hardware systems
- Edge ML engineers crafting on-device inference solutions
- Robotics teams implementing agentic AI for autonomous operations
21 Hours
