Edge AI Training in Virginia

This instructor-led, live training in Virginia (online or onsite) is aimed at advanced-level AI researchers, data scientists, and security specialists who wish to implement federated learning techniques for government, to train AI models across multiple edge devices while preserving data privacy.

By the end of this training, participants will be able to:

• Understand the principles and benefits of federated learning in Edge AI for government applications.
• Implement federated learning models using TensorFlow Federated and PyTorch for government projects.
• Optimize AI training across distributed edge devices in public sector environments.
• Address data privacy and security challenges specific to federated learning in the public sector.
• Deploy and monitor federated learning systems in real-world government applications.

This instructor-led, live training in Virginia (online or onsite) is aimed at beginner to intermediate agritech professionals, IoT specialists, and AI engineers who wish to develop and deploy Edge AI solutions for smart farming.

By the end of this training, participants will be able to:

• Understand the role of Edge AI in precision agriculture for government applications.
• Implement AI-driven systems for monitoring crops and livestock.
• Develop automated irrigation and environmental sensing solutions.
• Optimize agricultural efficiency using real-time Edge AI analytics for government initiatives.

This instructor-led, live training in Virginia (online or onsite) is aimed at advanced-level cybersecurity professionals, AI engineers, and IoT developers who wish to implement robust security measures and resilience strategies for Edge AI systems for government.

By the end of this training, participants will be able to:

• Understand security risks and vulnerabilities in Edge AI deployments for government.
• Implement encryption and authentication techniques for data protection in government settings.
• Design resilient Edge AI architectures that can withstand cyber threats for government operations.
• Apply secure AI model deployment strategies in edge environments for government use.

This instructor-led, live training in Virginia (online or onsite) is designed for government employees at the beginner to intermediate level who are retail technologists, AI developers, and business analysts. The goal is to apply Edge AI solutions for enhancing smart checkout systems, inventory management, and personalized customer engagement.

By the end of this training, participants will be able to:

• Understand how Edge AI improves retail operations and customer experience in government settings.
• Implement AI-powered smart checkout and cashier-less payment systems for government use.
• Optimize inventory management with real-time tracking and analytics for government applications.
• Utilize computer vision and AI to create personalized in-store experiences for government services.

This instructor-led, live training in Virginia (online or onsite) is designed for intermediate-level telecom professionals, artificial intelligence engineers, and Internet of Things specialists who seek to understand how 5G networks enhance Edge AI applications.

By the end of this training, participants will be able to:

• Comprehend the foundational aspects of 5G technology and its influence on Edge AI.
• Deploy AI models tailored for low-latency operations in 5G settings.
• Develop real-time decision-making systems leveraging Edge AI and 5G connectivity.
• Optimize AI tasks for efficient performance on edge devices, ensuring alignment with public sector workflows for government.

This instructor-led, live training in Virginia (online or onsite) is aimed at intermediate-level embedded AI developers and edge computing specialists who wish to fine-tune and optimize lightweight AI models for deployment on resource-constrained devices for government.

By the end of this training, participants will be able to:

• Select and adapt pre-trained models suitable for edge deployment in public sector applications.
• Apply quantization, pruning, and other compression techniques to reduce model size and latency for government use.
• Fine-tune models using transfer learning to enhance task-specific performance for government projects.
• Deploy optimized models on real edge hardware platforms that meet public sector requirements.

This instructor-led, live training in Virginia (online or onsite) is aimed at intermediate to advanced computer vision engineers, artificial intelligence developers, and Internet of Things professionals who wish to implement and optimize computer vision models for real-time processing on edge devices.

By the end of this training, participants will be able to:

• Understand the foundational principles of Edge AI and its applications in computer vision for government.
• Deploy optimized deep learning models on edge devices for real-time image and video analysis.
• Utilize frameworks such as TensorFlow Lite, OpenVINO, and NVIDIA Jetson SDK for model deployment.
• Optimize AI models to ensure performance, power efficiency, and low-latency inference in public sector environments.

This instructor-led, live training in Virginia (online or onsite) is aimed at intermediate-level embedded engineers, Internet of Things (IoT) developers, and artificial intelligence (AI) researchers who wish to implement TinyML techniques for AI-powered applications on energy-efficient hardware.

By the end of this training, participants will be able to:

• Understand the foundational principles of TinyML and edge AI.
• Deploy lightweight AI models on microcontrollers for government use.
• Optimize AI inference to ensure low-power consumption.
• Integrate TinyML with real-world IoT applications in public sector environments.

This instructor-led, live training in Virginia (online or onsite) is designed for intermediate to advanced-level robotics engineers, AI developers, and automation specialists who wish to implement Edge AI solutions for government applications.

By the end of this training, participants will be able to:

• Understand the role of Edge AI in autonomous systems for government.
• Deploy AI models on edge devices to support real-time robotics operations.
• Optimize AI performance to ensure low-latency decision-making in critical environments.
• Integrate computer vision and sensor fusion techniques to enhance robotic autonomy for government use cases.

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.

This instructor-led, live training in Virginia (online or onsite) is designed for advanced-level AI engineers, embedded developers, and hardware engineers who seek to implement AI models on low-power devices while optimizing energy efficiency.

By the end of this training, participants will be able to:

• Comprehend the challenges associated with running AI on energy-efficient devices for government applications.
• Optimize neural networks for low-power inference in public sector environments.
• Employ quantization, pruning, and model compression techniques to enhance efficiency.
• Deploy AI models on edge hardware with minimal power consumption, ensuring alignment with government standards.

This instructor-led, live training in Virginia (online or onsite) is aimed at intermediate-level artificial intelligence developers, embedded engineers, and robotics engineers who wish to optimize and deploy AI models on NVIDIA Jetson platforms for edge applications for government.

By the end of this training, participants will be able to:

• Understand the fundamentals of edge AI and NVIDIA Jetson hardware for government use cases.
• Optimize AI models for deployment on edge devices in public sector environments.
• Utilize TensorRT for accelerating deep learning inference in government applications.
• Deploy AI models using JetPack SDK and ONNX Runtime to support efficient governance and accountability.

This instructor-led, live training in Virginia (online or onsite) is designed for intermediate-level AI developers, machine learning engineers, and system architects who aim to optimize AI models for deployment on edge devices.

By the end of this training, participants will be able to:

• Understand the challenges and requirements associated with deploying AI models on edge devices for government applications.
• Apply model compression techniques to reduce the size and complexity of AI models for efficient use in resource-constrained environments.
• Utilize quantization methods to enhance model efficiency on edge hardware, ensuring optimal performance and resource utilization.
• Implement pruning and other optimization techniques to improve the overall performance of AI models deployed on edge devices.
• Deploy optimized AI models on a variety of edge devices, supporting enhanced decision-making and operational capabilities for government.

This instructor-led, live training in Virginia (online or onsite) is aimed at intermediate-level developers, data scientists, and technology professionals who wish to gain practical skills in deploying artificial intelligence (AI) models on edge devices for various applications.

By the end of this training, participants will be able to:

• Understand the principles of Edge AI and its benefits for government operations.
• Set up and configure the edge computing environment for government use.
• Develop, train, and optimize AI models for deployment on edge devices in public sector settings.
• Implement practical AI solutions on edge devices to enhance governmental workflows.
• Evaluate and improve the performance of edge-deployed models to meet government standards.
• Address ethical and security considerations specific to Edge AI applications in a government context.

This instructor-led, live training in Virginia (online or onsite) is designed for intermediate-level finance professionals, fintech developers, and artificial intelligence specialists who aim to implement Edge AI solutions within the financial sector.

By the end of this training, participants will be able to:

• Comprehend the role of Edge AI in financial services for government and private entities.
• Develop fraud detection systems utilizing Edge AI technology.
• Improve customer service through AI-driven solutions tailored for government and industry needs.
• Utilize Edge AI for risk management and decision-making processes in financial environments.
• Deploy and manage Edge AI solutions effectively in financial settings, ensuring alignment with public sector workflows and governance.

This instructor-led, live training in Virginia (online or onsite) is aimed at intermediate-level industrial engineers, manufacturing professionals, and AI developers who wish to implement Edge AI solutions for government and industry.

By the end of this training, participants will be able to:

• Understand the role of Edge AI in industrial automation for government applications.
• Implement predictive maintenance solutions using Edge AI to enhance operational efficiency.
• Apply AI techniques for quality control in manufacturing processes, ensuring compliance with regulatory standards.
• Optimize industrial processes using Edge AI to reduce costs and improve productivity.
• Deploy and manage Edge AI solutions in industrial environments, maintaining security and data integrity.

Edge AI involves the deployment of artificial intelligence models directly on devices and machines at the edge of the network, enabling real-time decision-making with minimal latency.

This instructor-led, live training (online or onsite) is designed for advanced-level embedded and IoT professionals who aim to deploy AI-powered logic and control systems in manufacturing environments where speed, reliability, and offline operation are essential for government applications.

By the end of this training, participants will be able to:

• Understand the architecture and benefits of edge AI systems for government.
• Build and optimize AI models for deployment on embedded devices.
• Utilize tools like TensorFlow Lite and OpenVINO for low-latency inference.
• Integrate edge intelligence with sensors, actuators, and industrial protocols.

Format of the Course

• Interactive lecture and discussion.
• Extensive exercises and practice sessions.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in Virginia (online or onsite) is aimed at intermediate-level developers, data scientists, and AI practitioners who wish to leverage TensorFlow Lite for government Edge AI applications.

By the end of this training, participants will be able to:

• Understand the fundamentals of TensorFlow Lite and its role in Edge AI for government.
• Develop and optimize AI models using TensorFlow Lite for government applications.
• Deploy TensorFlow Lite models on various edge devices for government use.
• Utilize tools and techniques for model conversion and optimization in a government context.
• Implement practical Edge AI applications using TensorFlow Lite for government operations.

This instructor-led, live training in Virginia (online or onsite) is designed for intermediate-level urban planners, civil engineers, and smart city project managers who aim to utilize Edge AI for government initiatives.

By the end of this training, participants will be able to:

• Comprehend the role of Edge AI in enhancing smart city infrastructures.
• Implement Edge AI solutions for traffic management and surveillance systems.
• Optimize urban resources through the application of Edge AI technologies.
• Integrate Edge AI with existing smart city systems to improve efficiency.
• Address ethical and regulatory considerations in smart city deployments for government.

This instructor-led, live training in Virginia (online or onsite) is designed for intermediate-level cybersecurity professionals, system administrators, and AI ethics researchers who aim to secure and ethically deploy Edge AI solutions for government.

By the end of this training, participants will be able to:

• Comprehend the security and privacy challenges inherent in Edge AI.
• Apply best practices for securing edge devices and data within public sector environments.
• Formulate strategies to mitigate security risks associated with Edge AI deployments in government settings.
• Address ethical considerations and ensure compliance with relevant regulations for government operations.
• Perform security assessments and audits for Edge AI applications to enhance governance and accountability.

This instructor-led, live training in Virginia (online or onsite) is designed for intermediate-level robotics engineers, autonomous vehicle developers, and AI researchers who seek to leverage Edge AI for innovative solutions in autonomous systems.

By the end of this training, participants will be able to:

• Understand the role and benefits of Edge AI in enhancing autonomous system performance.
• Develop and deploy AI models for real-time processing on edge devices, optimizing efficiency and responsiveness.
• Implement Edge AI solutions in a variety of applications, including autonomous vehicles, drones, and robotics, to improve operational capabilities.
• Design and optimize control systems using Edge AI to enhance accuracy and reliability.
• Address ethical and regulatory considerations specific to the deployment of autonomous AI applications for government and public sector use.

This instructor-led, live training in Virginia (online or onsite) is aimed at intermediate-level healthcare professionals, biomedical engineers, and artificial intelligence developers who wish to leverage Edge AI for innovative healthcare solutions for government.

By the end of this training, participants will be able to:

• Understand the role and benefits of Edge AI in healthcare for government.
• Develop and deploy AI models on edge devices for healthcare applications within public sector environments.
• Implement Edge AI solutions in wearable devices and diagnostic tools to enhance public health initiatives.
• Design and deploy patient monitoring systems using Edge AI to improve healthcare delivery for government.
• Address ethical and regulatory considerations specific to healthcare AI applications within the public sector.

Edge AI enables artificial intelligence models to run directly on embedded or resource-constrained devices, reducing latency and power consumption while enhancing autonomy and privacy in robotic systems.

This instructor-led, live training (online or onsite) is aimed at intermediate-level embedded developers and robotics engineers who wish to implement machine learning inference and optimization techniques directly on robotic hardware using TinyML and edge AI frameworks for government applications.

By the end of this training, participants will be able to:

• Understand the fundamentals of TinyML and edge AI for robotics in public sector contexts.
• Convert and deploy AI models for on-device inference in government environments.
• Optimize models for speed, size, and energy efficiency to meet public sector requirements.
• Integrate edge AI systems into robotic control architectures for government operations.
• Evaluate performance and accuracy in real-world scenarios relevant to government missions.

Format of the Course

• Interactive lecture and discussion tailored for government audiences.
• Hands-on practice using TinyML and edge AI toolchains adapted for public sector use.
• Practical exercises on embedded and robotic hardware platforms suitable for government applications.

Course Customization Options

• To request a customized training for this course, tailored to specific needs for government agencies, please contact us to arrange.

The 6G and the Intelligent Edge course is a forward-looking program that explores the integration of 6G wireless technologies with edge computing, IoT ecosystems, and AI-driven data processing to support intelligent, low-latency, and adaptive infrastructures for government.

This instructor-led, live training (available online or onsite) is designed for intermediate-level IT architects who wish to understand and design next-generation distributed architectures that leverage the synergy of 6G connectivity and intelligent edge systems.

Upon completion of this course, participants will be able to:

• Understand how 6G will transform edge computing and IoT architectures for government.
• Design distributed systems for ultra-low latency, high bandwidth, and autonomous operations in a public sector context.
• Integrate AI and data analytics at the edge to support intelligent decision-making for government applications.
• Plan scalable, secure, and resilient 6G-ready edge infrastructures that align with public sector workflows and governance.
• Evaluate business and operational models enabled by 6G-edge convergence in the context of government operations.

Format of the Course

• Interactive lectures and discussions focused on government applications.
• Case studies and applied architecture design exercises relevant to public sector challenges.
• Hands-on simulation with optional edge or container tools, tailored for government use cases.

Course Customization Options

• To request a customized training for this course, specifically designed to meet the needs of your government agency, please contact us to arrange.

This instructor-led, live training in Virginia (online or onsite) is aimed at advanced-level artificial intelligence (AI) practitioners, researchers, and developers who wish to master the latest advancements in Edge AI, optimize their AI models for edge deployment, and explore specialized applications across various industries.

By the end of this training, participants will be able to:

• Investigate advanced techniques in Edge AI model development and optimization.
• Implement cutting-edge strategies for deploying AI models on edge devices.
• Utilize specialized tools and frameworks for advanced Edge AI applications.
• Enhance the performance and efficiency of Edge AI solutions.
• Explore innovative use cases and emerging trends in Edge AI.
• Address advanced ethical and security considerations in Edge AI deployments, ensuring alignment with public sector workflows and governance for government.

Huawei's Ascend CANN toolkit facilitates robust AI inference on edge devices such as the Ascend 310. CANN offers essential tools for compiling, optimizing, and deploying models in environments with limited compute and memory resources.

This instructor-led, live training (online or onsite) is designed for intermediate-level AI developers and integrators who aim to deploy and optimize models on Ascend edge devices using the CANN toolchain for government applications.

By the end of this training, participants will be able to:

• Prepare and convert AI models for the Ascend 310 using CANN tools.
• Construct lightweight inference pipelines with MindSpore Lite and AscendCL.
• Enhance model performance in environments with constrained compute and memory.
• Deploy and monitor AI applications in real-world edge scenarios.

Format of the Course

• Interactive lecture and demonstration.
• Hands-on laboratory work with models and scenarios specific to edge devices.
• Live deployment examples on virtual or physical edge hardware.

Course Customization Options

• To request a customized training for this course, please contact Govtra to arrange.

This instructor-led, live training in Virginia (online or onsite) is designed for intermediate-level developers, system architects, and industry professionals who wish to leverage Edge AI to enhance IoT applications with advanced data processing and analytics capabilities.

By the end of this training, participants will be able to:

• Understand the foundational principles of Edge AI and its application in IoT systems for government.
• Set up and configure Edge AI environments suitable for IoT devices used in public sector workflows.
• Develop and deploy AI models on edge devices to support IoT applications aligned with government objectives.
• Implement real-time data processing and decision-making capabilities in IoT systems for enhanced governance and accountability.
• Integrate Edge AI with various IoT protocols and platforms commonly used in the public sector.
• Address ethical considerations and best practices in Edge AI deployment for government applications.

This instructor-led, live training in Virginia (online or onsite) is aimed at intermediate-level IoT developers, embedded engineers, and AI practitioners who wish to implement TinyML for government applications such as predictive maintenance, anomaly detection, and smart sensor deployments.

By the end of this training, participants will be able to:

• Understand the fundamentals of TinyML and its applications in IoT for government.
• Set up a TinyML development environment suitable for government IoT projects.
• Develop and deploy ML models on low-power microcontrollers for government use cases.
• Implement predictive maintenance and anomaly detection using TinyML in government contexts.
• Optimize TinyML models for efficient power and memory usage in government applications.

This instructor-led, live training in Virginia (online or onsite) is designed for intermediate-level developers and IT professionals who wish to gain a thorough understanding of Edge AI, from foundational concepts to practical implementation, including setup and deployment.

By the end of this training, participants will be able to:

• Grasp the fundamental principles of Edge AI.
• Set up and configure Edge AI environments for government use.
• Develop, train, and optimize Edge AI models.
• Deploy and manage Edge AI applications in a secure and efficient manner.
• Integrate Edge AI with existing systems and workflows to enhance operational effectiveness.
• Address ethical considerations and best practices in the implementation of Edge AI for government operations.

This instructor-led, live training in Virginia (online or onsite) is designed for intermediate-level embedded systems engineers and artificial intelligence developers who wish to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.

By the end of this training, participants will be able to:

• Understand the foundational principles of TinyML and its advantages for edge AI applications in various sectors, including those for government.
• Establish a development environment suitable for TinyML projects.
• Train, optimize, and deploy artificial intelligence models on low-power microcontrollers.
• Utilize TensorFlow Lite and Edge Impulse to develop practical TinyML applications.
• Enhance AI models for improved power efficiency and memory management.

Cambricon MLUs (Machine Learning Units) are specialized AI chips designed to optimize inference and training in edge and data center environments.

This instructor-led, live training (online or onsite) is aimed at intermediate-level developers who wish to build and deploy AI models using the BANGPy framework and Neuware SDK on Cambricon MLU hardware for government applications.

By the end of this training, participants will be able to:

• Set up and configure the BANGPy and Neuware development environments for government use.
• Develop and optimize Python- and C++-based models for Cambricon MLUs in alignment with public sector workflows.
• Deploy models to edge and data center devices running Neuware runtime, ensuring compliance with governance standards.
• Integrate machine learning workflows with MLU-specific acceleration features to enhance performance and accountability.

Format of the Course

• Interactive lecture and discussion focused on government applications.
• Hands-on use of BANGPy and Neuware for development and deployment in a public sector context.
• Guided exercises centered on optimization, integration, and testing tailored to government needs.

Course Customization Options

• To request a customized training for this course based on your Cambricon device model or specific use case for government, please contact us to arrange.

This instructor-led, live training in Virginia (online or onsite) is aimed at beginner-level developers and IT professionals who wish to gain a foundational understanding of Edge AI and its introductory applications for government.

By the end of this training, participants will be able to:

• Understand the basic concepts and architecture of Edge AI as they apply to public sector workflows.
• Set up and configure Edge AI environments in alignment with government standards.
• Develop and deploy simple Edge AI applications that enhance governance and accountability.
• Identify and understand the use cases and benefits of Edge AI for government operations.

Integrate the transformative capabilities of artificial intelligence (AI) with the agility of edge computing in this comprehensive course designed for government professionals. Participants will learn to deploy AI models directly on edge devices, covering topics from understanding convolutional neural network (CNN) architectures to mastering knowledge distillation and federated learning. This hands-on training will equip you with the skills necessary to optimize AI performance for real-time processing and decision-making at the edge, ensuring alignment with public sector workflows, governance, and accountability.