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Course Outline
Introduction to Edge and Agentic AI
- Overview of agentic AI and edge computing
- Considerations regarding latency, privacy, and bandwidth
- Architectural comparison: cloud-based vs. edge-based agents
Designing Lightweight Agent Architectures
- Decomposing 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
- Establishing test environments on Raspberry Pi or similar hardware
Implementing On-Device Inference
- Converting and quantizing models for edge deployment
- Executing inference using TensorFlow Lite and ONNX Runtime
- Incorporating inference outcomes into agent decision loops
Integrating Agents with Hardware and IoT
- Linking sensors, actuators, and IoT modules
- Local data acquisition and processing pipelines
- Offline functionality 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
Practical 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 knowledge of machine learning workflows
- Understanding of 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
