Edge & Lightweight Agents: On-Device Agentic Workloads with Python Training Course

Introduction to Edge and Agentic AI

• Foundations of agentic AI and edge computing
• Considerations for latency, privacy, and bandwidth
• Comparing cloud-based and edge-based agent architectures

Designing Lightweight Agent Architectures

• Deconstructing the agent loop for resource-constrained systems
• Leveraging asynchronous design for computational efficiency
• Striking a balance between autonomy and network connectivity

Setting Up the Development Environment

• Installing Python libraries for edge AI
• Configuring TensorFlow Lite and PyTorch Mobile
• Establishing test environments on Raspberry Pi or comparable devices

Implementing On-Device Inference

• Model conversion and quantization for edge deployment
• Executing inference via TensorFlow Lite and ONNX Runtime
• Embedding inference outputs into agent decision-making loops

Integrating Agents with Hardware and IoT

• Linking sensors, actuators, and IoT modules
• Building local data collection and processing pipelines
• Enabling offline functionality and event-triggered responses

Optimization and Monitoring

• Tuning for low power consumption and high speed
• Applying edge caching and model compression strategies
• Monitoring and troubleshooting edge agents

Hands-on Project: Deploying a Lightweight Agent on Edge Hardware

• Creating a compact autonomous agent for IoT or robotics applications
• Developing model inference and local logic modules
• Testing and refining for optimal latency and reliability

Summary and Next Steps