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Course Outline
Introduction to Edge and Agentic AI
- Fundamentals of agentic AI and edge computing
- Key considerations regarding latency, privacy, and bandwidth
- Architectural analysis: cloud-based versus edge-based agents
Architecting Lightweight Agent Systems
- Simplifying agent loops for resource-constrained systems
- Utilizing asynchronous design for computational efficiency
- Striking a balance between autonomy and connectivity
Establishing the Development Environment
- Setting up Python frameworks for edge AI
- Configuring TensorFlow Lite and PyTorch Mobile
- Deploying test environments on Raspberry Pi or comparable devices
Executing On-Device Inference
- Converting and quantizing models for edge deployment
- Running inference via TensorFlow Lite and ONNX Runtime
- Incorporating inference outcomes into agent decision-making loops
Connecting Agents to Hardware and IoT
- Linking sensors, actuators, and IoT modules
- Building local data collection and processing pipelines
- Enabling offline operation and event-driven behaviors
Optimization and Monitoring Strategies
- Tuning for low power consumption and high-speed performance
- Applying edge caching and model compression methods
- Monitoring and troubleshooting edge agents
Practical 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 optimal latency and reliability
Wrap-up and Future Directions
Requirements
- Proficiency in Python programming
- Fundamental knowledge of machine learning pipelines
- Working familiarity with embedded or edge computing principles
Target Audience
- Embedded developers integrating AI capabilities into hardware systems
- Edge ML engineers specializing in on-device inference solutions
- Robotics teams implementing agentic AI for autonomous functions
21 Hours
