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Course Outline
Introduction to Edge and Agentic AI
- Overview of agentic AI and edge computing landscapes
- Key considerations for latency, privacy, and bandwidth
- Architectural analysis: cloud-based vs. edge-based agents
Designing Lightweight Agent Architectures
- Deconstructing the agent loop for constrained systems
- Asynchronous design patterns for efficient computation
- Striking a balance between autonomy and connectivity
Configuring the Development Environment
- Installing Python frameworks tailored for edge AI
- Setting up TensorFlow Lite and PyTorch Mobile
- Establishing test environments on Raspberry Pi or comparable hardware
Implementing On-Device Inference
- Model conversion and quantization strategies for edge deployment
- Executing inference via TensorFlow Lite and ONNX Runtime
- Seamlessly integrating inference outputs into agent decision loops
Integrating Agents with Hardware and IoT
- Linking sensors, actuators, and IoT modules
- Building local data collection and processing pipelines
- Enabling 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 and debugging edge-based 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 structures
- Testing and refining for optimal latency and reliability
Summary and Future Directions
Requirements
- Proficiency in Python programming
- Fundamental comprehension of machine learning workflows
- Knowledge 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
