Edge & Lightweight Agents: On-Device Agentic Workloads with Python Training Course
The Edge & Lightweight Agents course offers practical guidance on deploying agentic AI workloads on devices with limited resources. Participants will learn to construct, optimize, and manage lightweight agents that perform local reasoning and inference, thereby enhancing speed, privacy, and reliability in distributed settings. The curriculum highlights performance tuning, low-latency design, and the integration of hardware and software.
This live, instructor-led training (available online or onsite) targets intermediate-level professionals looking to implement and optimize on-device agentic systems using Python and edge AI frameworks.
Upon completing this training, participants will be capable of:
- Grasping the architecture and challenges of running agentic AI on edge devices.
- Designing lightweight agent loops appropriate for constrained environments.
- Implementing local inference using TensorFlow Lite, PyTorch Mobile, and ONNX.
- Integrating agents with sensors, actuators, and IoT platforms.
- Optimizing performance, energy consumption, and latency for real-time operations.
Course Format
- Interactive lectures combined with practical demonstrations.
- Hands-on development within local or emulated environments.
- Project-based learning alongside guided implementation exercises.
Customization Options
- To request a tailored training for this course, please reach out to us to make arrangements.
Course Outline
Introduction to Edge and Agentic AI
- Overview of agentic AI and edge computing.
- Considerations regarding latency, privacy, and bandwidth.
- Architectural comparison between cloud and edge agents.
Designing Lightweight Agent Architectures
- Structuring the agent loop for constrained systems.
- Asynchronous design for efficient computation.
- Balancing autonomy and connectivity.
Setting Up the Development Environment
- Installing Python frameworks for edge AI.
- Configuring TensorFlow Lite and PyTorch Mobile.
- Deploying test environments on Raspberry Pi or similar devices.
Implementing On-Device Inference
- Converting and quantizing models for edge deployment.
- Running inference with TensorFlow Lite and ONNX Runtime.
- Integrating inference results into agent decision loops.
Integrating Agents with Hardware and IoT
- Connecting sensors, actuators, and IoT modules.
- Establishing local data collection and processing pipelines.
- Enabling offline operation and event-triggered behavior.
Optimization and Monitoring
- Performance tuning for low power consumption and high speed.
- Techniques for edge caching and model compression.
- Monitoring and debugging edge agents.
Hands-on Project: Deploying a Lightweight Agent on Edge Hardware
- Designing a small autonomous agent for an IoT or robotics task.
- Implementing model inference and local logic.
- Testing and optimizing for latency and reliability.
Summary and Next Steps
Requirements
- Experience with Python programming.
- A foundational understanding of machine learning workflows.
- Familiarity with concepts related to embedded or edge computing.
Audience
- Embedded developers integrating AI into hardware systems.
- Edge ML engineers designing on-device inference solutions.
- Robotics teams deploying agentic AI for autonomous operation.
Open Training Courses require 5+ participants.
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