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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
- Architectural comparison: cloud vs. edge agents
Designing Lightweight Agent Architectures
- Deconstructing the agent loop for constrained systems
- Asynchronous design for efficient computation
- Striking a balance between autonomy and 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 similar devices
Implementing On-Device Inference
- Converting and quantizing models for edge deployment
- Executing inference with TensorFlow Lite and ONNX Runtime
- Integrating inference results into agent decision loops
Integrating Agents with Hardware and IoT
- Connecting sensors, actuators, and IoT modules
- Local data collection and processing pipelines
- Offline operation and event-triggered behavior
Optimization and Monitoring
- Performance tuning for low power 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 small autonomous agent for an IoT or robotics task
- Implementing model inference and local logic
- Testing and optimizing for latency and reliability
Summary and Next Steps
Requirements
- Proficiency in Python programming
- Foundational knowledge of machine learning workflows
- Acquaintance with embedded or edge computing principles
Target Audience
- Embedded developers integrating AI capabilities into hardware systems
- Edge ML engineers creating on-device inference solutions
- Robotics teams deploying agentic AI for autonomous operations
21 Hours
