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Course Outline
Introduction to Edge and Agentic AI
- Fundamentals of agentic AI and edge computing
- Considerations regarding latency, privacy, and bandwidth
- Comparative architectural analysis: cloud-based vs. edge-based agents
Designing Lightweight Agent Architectures
- Deconstructing the agent loop for constrained systems
- Employing asynchronous design for computational efficiency
- Striking a balance between autonomy and connectivity
Setting Up the Development Environment
- Installation of Python frameworks suitable for edge AI
- Configuration of TensorFlow Lite and PyTorch Mobile
- Deployment of 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 outputs into agent decision-making loops
Integrating Agents with Hardware and IoT
- Linking sensors, actuators, and IoT modules
- Building local data collection and processing pipelines
- Managing offline operation and event-triggered behaviors
Optimization and Monitoring
- Performance tuning for low power consumption and high speed
- Techniques for edge caching and model compression
- Monitoring strategies and debugging for 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 alongside local logic
- Testing and optimizing for latency and reliability
Summary and Next Steps
Requirements
- Proficiency in Python programming
- Foundational knowledge of machine learning workflows
- Aquaintance 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 implementing agentic AI for autonomous operations
21 Hours
