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Course Outline
Introduction to Edge and Agentic AI
- Fundamentals of agentic AI and edge computing
- Factors affecting latency, privacy, and bandwidth
- Architectural differences: cloud-based vs. edge-based agents
Designing Lightweight Agent Architectures
- Structuring agent loops for constrained systems
- Asynchronous design strategies 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
- Establishing test environments on Raspberry Pi or similar hardware
Implementing On-Device Inference
- Model conversion and quantization for edge deployment
- Executing inference with TensorFlow Lite and ONNX Runtime
- Integrating inference outcomes into agent decision-making processes
Integrating Agents with Hardware and IoT
- Linking sensors, actuators, and IoT modules
- Local data acquisition and processing workflows
- Offline capabilities and event-driven behaviors
Optimization and Monitoring
- Tuning for low power consumption and high speed
- Edge caching strategies and model compression methods
- Monitoring and debugging edge-based agents
Hands-on Project: Deploying a Lightweight Agent on Edge Hardware
- Designing a small autonomous agent for IoT or robotics applications
- Implementing model inference and local logic
- Testing and optimizing for latency and reliability
Summary and Next Steps
Requirements
- Proficiency in Python programming
- Fundamental grasp of machine learning workflows
- Knowledge of embedded or edge computing principles
Target Audience
- Embedded developers incorporating AI into hardware systems
- Edge ML engineers crafting on-device inference solutions
- Robotics teams deploying agentic AI for autonomous tasks
21 Hours
