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Course Outline
Introduction to Edge and Agentic AI
- Overview of agentic AI and edge computing fundamentals
- Key considerations regarding latency, privacy, and bandwidth
- Comparing cloud-based and edge-based agent architectures
Designing Lightweight Agent Architectures
- Deconstructing the agent loop for constrained systems
- Designing asynchronous workflows for computational efficiency
- Striking a balance between autonomy and network connectivity
Setting Up the Development Environment
- Installing Python frameworks suited for edge AI
- Configuring TensorFlow Lite and PyTorch Mobile environments
- Deploying test environments on devices such as Raspberry Pi
Implementing On-Device Inference
- Converting and quantizing models for edge deployment
- Executing inference using TensorFlow Lite and ONNX Runtime
- Incorporating inference outputs into agent decision-making loops
Integrating Agents with Hardware and IoT
- Linking sensors, actuators, and IoT modules
- Establishing local data collection and processing pipelines
- Managing offline operation and event-driven behaviors
Optimization and Monitoring
- Tuning performance for low power consumption and high speed
- Applying edge caching and model compression techniques
- Monitoring and debugging edge-based agents
Hands-on Project: Deploying a Lightweight Agent on Edge Hardware
- Designing a compact autonomous agent for IoT or robotics applications
- Implementing model inference and local logic modules
- Testing and refining for optimal latency and reliability
Summary and Next Steps
Requirements
- Practical experience 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 creating on-device inference solutions
- Robotics teams deploying agentic AI for autonomous functions
21 Hours
