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Course Outline
Introduction to Edge and Agentic AI
- Foundations of agentic AI and edge computing
- Key factors: latency, privacy, and bandwidth
- Comparing cloud-based versus edge-based agent architectures
Architecting Lightweight Agent Systems
- Deconstructing the agent loop for constrained systems
- Utilizing asynchronous design for computational efficiency
- Striking a balance between autonomy and network connectivity
Configuring the Development Environment
- Setting up Python frameworks for edge AI
- Setting up TensorFlow Lite and PyTorch Mobile
- Establishing test environments on Raspberry Pi or comparable hardware
Executing On-Device Inference
- Model conversion and quantization for edge deployment
- Running inference via TensorFlow Lite and ONNX Runtime
- Incorporating inference outputs into agent decision-making cycles
Connecting Agents to Hardware and IoT
- Linking sensors, actuators, and IoT modules
- Building local data collection and processing pipelines
- Enabling offline operation and event-driven behaviors
Performance Optimization and Monitoring
- Tuning for low power consumption and high speed
- Techniques for edge caching and model compression
- Monitoring and debugging edge-based agents
Practical 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 latency and system reliability
Conclusion and Path Forward
Requirements
- Proficiency in Python programming
- Fundamental knowledge of machine learning workflows
- Akquainted with embedded or edge computing principles
Target Audience
- Embedded developers integrating AI capabilities into hardware systems
- Edge ML engineers developing on-device inference solutions
- Robotics teams implementing agentic AI for autonomous operations
21 Hours