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Course Outline
Introduction to Edge and Agentic AI
- Overview of agentic AI and edge computing concepts
- Key considerations regarding latency, privacy, and bandwidth
- Architectural differences between cloud-based and edge-based agents
Designing Lightweight Agent Architectures
- Deconstructing the agent loop for optimized performance in constrained systems
- Leveraging asynchronous design for efficient computation
- Striking a balance between autonomy and connectivity
Establishing the Development Environment
- Setting up Python frameworks optimized for edge AI
- Configuring TensorFlow Lite and PyTorch Mobile
- Deploying test environments on devices such as Raspberry Pi
Implementing On-Device Inference
- Converting and quantizing models for effective edge deployment
- Executing inference with TensorFlow Lite and ONNX Runtime
- Incorporating inference outputs into the agent’s decision-making loop
Integrating Agents with Hardware and IoT
- Linking sensors, actuators, and IoT modules
- Building local data collection and processing pipelines
- Enabling 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
- Testing and optimizing for minimal latency and high reliability
Summary and Next Steps
Requirements
- Proficiency in Python programming
- Fundamental grasp of machine learning workflows
- Basic knowledge of embedded or edge computing principles
Target Audience
- Embedded developers looking to incorporate AI into hardware systems
- Edge ML engineers focused on creating on-device inference solutions
- Robotics teams deploying agentic AI for autonomous tasks
21 Hours
