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

Introduction to Edge and Agentic AI

• Foundations of agentic AI and edge computing
• Key considerations for latency, privacy, and bandwidth
• Comparative analysis of cloud-based versus edge-based agent architectures

Designing Lightweight Agent Architectures

• Structuring agent loops for constrained systems
• Employing asynchronous design for computational efficiency
• Striking a balance between autonomy and connectivity

Setting Up the Development Environment

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

Implementing On-Device Inference

• Model conversion and quantization for edge deployment
• Executing inference via 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
• Building local data collection and processing workflows
• Enabling offline operation and event-driven responses

Optimization and Monitoring

• Tuning performance for low power consumption and high speed
• Applying edge caching and model compression strategies
• 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
• Testing and refining for optimal latency and reliability

Summary and Next Steps