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Course Outline
Introduction to Edge and Agentic AI
- An overview of agentic AI and edge computing concepts
- Factors affecting latency, privacy, and bandwidth
- Comparing architectures: cloud-based versus edge-based agents
Designing Lightweight Agent Architectures
- Decomposing the agent loop for constrained systems
- Employing asynchronous design for computational efficiency
- Striking a balance between autonomy and connectivity
Preparing the Development Environment
- Installing Python frameworks suited for edge AI
- Configuring TensorFlow Lite and PyTorch Mobile
- Establishing test environments on Raspberry Pi or equivalent hardware
Implementing On-Device Inference
- Converting and quantizing models for edge deployment
- Executing inference with 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
- Creating local data collection and processing pipelines
- Enabling offline operation and event-driven behavior
Optimization and Monitoring
- Tuning performance for low power consumption and high speed
- Applying edge caching and model compression methods
- 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 tasks
- Implementing model inference and local logic
- Testing and refining for latency and reliability
Summary and Next Steps
Requirements
- Practical experience in Python programming
- A foundational understanding of machine learning processes
- Acquaintance with embedded or edge computing principles
Target Audience
- Embedded developers integrating AI capabilities into hardware systems
- Edge ML engineers crafting on-device inference solutions
- Robotics teams deploying agentic AI for autonomous functions
21 Hours
