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Duration 21 hours
Course Outline
Introduction to Edge and Agentic AI
- Foundations of agentic AI and edge computing
- Considerations regarding latency, privacy, and bandwidth
- Architectural analysis: cloud-based agents versus edge agents
Designing Lightweight Agent Architectures
- Deconstructing the agent loop for resource-constrained systems
- Asynchronous design patterns for computational efficiency
- Balancing agent autonomy with connectivity requirements
Setting Up the Development Environment
- Installing necessary Python frameworks for edge AI
- Configuring TensorFlow Lite and PyTorch Mobile
- Establishing test environments on Raspberry Pi or comparable hardware
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-making loops
Integrating Agents with Hardware and IoT
- Connecting sensors, actuators, and IoT modules
- Building local data collection and processing pipelines
- Implementing 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 edge-based agents
Hands-on Project: Deploying a Lightweight Agent on Edge Hardware
- Designing a small autonomous agent for specific IoT or robotics tasks
- Implementing model inference and local logic
- Testing and refining for latency and reliability
Summary and Next Steps
Requirements
- Proficiency in Python programming
- Fundamental knowledge of machine learning workflows
- Familiarity with embedded systems or edge computing concepts
Target Audience
- Embedded developers seeking to integrate AI capabilities into hardware systems
- Edge ML engineers developing on-device inference solutions
- Robotics teams implementing agentic AI for autonomous operations
