Edge & Lightweight Agents: On-Device Agentic Workloads with Python Training Course
The Edge & Lightweight Agents course is designed to provide practical instruction on deploying agentic artificial intelligence (AI) workloads on resource-constrained devices. Participants will learn how to build, optimize, and manage lightweight agents capable of performing local reasoning and inference, which enhances speed, privacy, and reliability in distributed environments. The course emphasizes performance tuning, low-latency design, and hardware-software integration.
This instructor-led, live training (online or onsite) is targeted at intermediate-level professionals who aim to implement and optimize on-device agentic systems using Python and edge AI frameworks for government applications.
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 suitable for environments with limited resources.
- Implement local inference using TensorFlow Lite, PyTorch Mobile, and ONNX.
- Integrate agents with sensors, actuators, and IoT platforms.
- Optimize performance, energy use, and latency for real-time operation in public sector workflows.
Format of the Course
- Interactive lecture and practical demonstrations.
- Hands-on development in local or emulated environments.
- Project-based learning and guided implementation exercises.
Course Customization Options
- To request a customized training for this course, please contact Govtra to arrange.
Course Outline
Introduction to Edge and Agentic AI
- Overview of agentic AI and edge computing for government applications
- Latency, privacy, and bandwidth considerations in public sector environments
- Architectural comparison: cloud vs. edge agents for government use cases
Designing Lightweight Agent Architectures for Government
- Breaking down the agent loop for constrained systems in public sector workflows
- Asynchronous design for efficient computation to enhance governmental operations
- Balancing autonomy and connectivity to support government governance and accountability
Setting Up the Development Environment for Government
- Installing Python frameworks for edge AI in public sector projects
- 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
- Converting and quantizing models for edge deployment in public sector settings
- Running inference with TensorFlow Lite and ONNX Runtime to support government operations
- Integrating inference results into agent decision loops for enhanced governmental processes
Integrating Agents with Hardware and IoT for Government
- Connecting sensors, actuators, and IoT modules in government systems
- Local data collection and processing pipelines to support public sector workflows
- Offline operation and event-triggered behavior for reliable government applications
Optimization and Monitoring for Government
- Performance tuning for low power and high speed in governmental contexts
- Edge caching and model compression techniques to enhance public sector efficiency
- Monitoring and debugging edge agents to ensure reliable government operations
Hands-on Project: Deploying a Lightweight Agent on Edge Hardware for Government
- Designing a small autonomous agent for an IoT or robotics task in public sector applications
- Implementing model inference and local logic to support government tasks
- Testing and optimizing for latency and reliability in governmental settings
Summary and Next Steps for Government
Requirements
- Experience with Python programming for government applications
- Basic understanding of machine learning workflows and their application in the public sector
- 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 government projects
- Robotics teams deploying agentic AI for autonomous operation in government settings
Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.
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