Edge & Lightweight Agents: On-Device Agentic Workloads with Python Training Course
The Edge & Lightweight Agents course is designed to provide practical training in deploying agentic artificial intelligence (AI) workloads on resource-constrained devices. Participants will learn to develop, optimize, and manage lightweight agents capable of performing local reasoning and inference, thereby enhancing speed, privacy, and reliability in distributed environments. The course places a strong emphasis on performance tuning, low-latency design, and the integration of hardware and software.
This instructor-led, live training (available both online and onsite) is tailored for intermediate-level professionals who are interested in implementing and optimizing on-device agentic systems using Python and edge AI frameworks.
By the end of this training, participants will be able to:
- Understand the architecture and challenges associated with running agentic AI on edge devices.
- Design lightweight agent loops that are suitable for environments with limited resources.
- Implement local inference capabilities using TensorFlow Lite, PyTorch Mobile, and ONNX.
- Integrate agents with sensors, actuators, and Internet of Things (IoT) platforms.
- Optimize performance, energy consumption, and latency to ensure real-time operation.
**Format of the Course**
- Interactive lectures and practical demonstrations.
- Hands-on development in local or emulated environments.
- Project-based learning and guided implementation exercises.
**Course Customization Options for Government**
To request a customized training program tailored to specific needs and requirements, please contact us to arrange. This option is particularly useful for government agencies looking to align the course content with their unique workflows, governance, and accountability standards.This course is available as onsite live training in US Government or online live training.
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Course Outline
Introduction to Edge and Agentic AI for Government
- Overview of agentic artificial intelligence (AI) and edge computing for government operations
- Latency, privacy, and bandwidth considerations in public sector applications
- Architectural comparison: cloud vs. edge agents in government systems
Designing Lightweight Agent Architectures for Government Use
- Breaking down the agent loop for constrained government systems
- Asynchronous design for efficient computation in public sector environments
- Balancing autonomy and connectivity for enhanced government operations
Setting Up the Development Environment for Government Projects
- Installing Python frameworks for edge AI in government systems
- Configuring TensorFlow Lite and PyTorch Mobile for government applications
- Deploying test environments on Raspberry Pi or similar devices for government testing
Implementing On-Device Inference for Government Use
- Converting and quantizing models for edge deployment in government systems
- Running inference with TensorFlow Lite and ONNX Runtime for government applications
- Integrating inference results into agent decision loops for enhanced government operations
Integrating Agents with Hardware and IoT for Government
- Connecting sensors, actuators, and IoT modules in government infrastructure
- Local data collection and processing pipelines for government use
- Offline operation and event-triggered behavior for robust government applications
Optimization and Monitoring for Government Systems
- Performance tuning for low power and high speed in government edge devices
- Edge caching and model compression techniques for efficient government operations
- Monitoring and debugging edge agents to ensure reliability in government systems
Hands-on Project: Deploying a Lightweight Agent on Edge Hardware for Government
- Designing a small autonomous agent for an IoT or robotics task in government operations
- Implementing model inference and local logic for government applications
- Testing and optimizing for latency and reliability in government systems
Summary and Next Steps for Government Initiatives
Requirements
- Experience with Python programming for government applications
- Basic understanding of machine learning workflows and their implementation in public sector projects
- Familiarity with embedded or edge computing concepts, particularly as they relate to government technology solutions
Audience
- Embedded developers integrating AI into hardware systems for government use
- Edge ML engineers designing on-device inference solutions for public sector deployments
- Robotics teams deploying agentic AI for autonomous operation in government environments
21 Hours
Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.
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