Edge AI Training in Virginia

This instructor-led, live training (offered online or on-site) is designed for advanced-level AI researchers, data scientists, and security specialists who aim to implement federated learning techniques for government. The course focuses on training AI models across multiple edge devices while ensuring data privacy. By the end of this training, participants will be able to: - Understand the principles and benefits of federated learning in Edge AI. - Implement federated learning models using TensorFlow Federated and PyTorch. - Optimize AI training processes across distributed edge devices. - Address data privacy and security challenges inherent in federated learning. - Deploy and monitor federated learning systems in real-world applications for government.

This instructor-led, live training in [location] (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 for government. By the end of this training, participants will be able to: - Understand the role of Edge AI in precision agriculture. - Implement AI-driven crop and livestock monitoring systems. - Develop automated irrigation and environmental sensing solutions. - Optimize agricultural efficiency using real-time Edge AI analytics.

This instructor-led, live training (available online or onsite) is designed for advanced-level cybersecurity professionals, AI engineers, and IoT developers who aim to implement robust security measures and resilience strategies for Edge AI systems. By the end of this training, participants will be able to: - Understand the security risks and vulnerabilities associated with Edge AI deployments. - Implement encryption and authentication methods to protect data. - Design resilient Edge AI architectures capable of withstanding cyber threats. - Apply secure deployment strategies for AI models in edge environments, ensuring alignment with public sector workflows and governance for government.

This instructor-led, live training (online or onsite) is designed for government personnel, including beginner to intermediate-level retail technologists, AI developers, and business analysts who wish to apply Edge AI solutions for smart checkout systems, inventory management, and personalized customer engagement. By the end of this training, participants will be able to: - Understand how Edge AI enhances retail operations and customer experience. - Implement AI-powered smart checkout and cashier-less payment systems. - Optimize inventory management with real-time tracking and analytics. - Utilize computer vision and AI for personalized in-store experiences.

This instructor-led, live training in [location] (online or onsite) is aimed at intermediate-level telecom professionals, AI engineers, and IoT specialists who wish to explore how 5G networks accelerate Edge AI applications for government. By the end of this training, participants will be able to: - Understand the fundamentals of 5G technology and its impact on Edge AI. - Deploy AI models optimized for low-latency applications in 5G environments. - Implement real-time decision-making systems using Edge AI and 5G connectivity. - Optimize AI workloads for efficient performance on edge devices.

This instructor-led, live training (conducted 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. By the end of this training, participants will be able to: - Select and adapt pre-trained models suitable for edge deployment. - Apply quantization, pruning, and other compression techniques to reduce model size and latency. - Fine-tune models using transfer learning to enhance task-specific performance. - Deploy optimized models on real edge hardware platforms, ensuring alignment with public sector workflows and governance standards for government.

This instructor-led, live training in [location] (online or onsite) is aimed at intermediate to advanced computer vision engineers, AI developers, and IoT 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 fundamentals of Edge AI and its applications in computer vision. - 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 for performance, power efficiency, and low-latency inference. This training is designed to enhance the skills of professionals in the public sector, ensuring they are well-equipped to leverage advanced technologies for government applications.

This instructor-led, live training in [location] (online or onsite) is aimed at intermediate-level embedded engineers, IoT developers, and AI researchers who wish to implement TinyML techniques for government applications on energy-efficient hardware. By the end of this training, participants will be able to: - Understand the fundamentals of TinyML and edge AI. - Deploy lightweight AI models on microcontrollers. - Optimize AI inference for low-power consumption. - Integrate TinyML with real-world IoT applications.

This instructor-led, live training in [location] (online or onsite) is aimed at intermediate to advanced robotics engineers, AI developers, and automation specialists who wish to implement Edge AI for government robotics applications. By the end of this training, participants will be able to: - Understand the role of Edge AI in autonomous systems. - Deploy AI models on edge devices for real-time robotic operations. - Optimize AI performance for low-latency decision-making. - Integrate computer vision and sensor fusion for enhanced robotic autonomy.

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 instructor-led, live training in [location] (online or onsite) is designed for advanced-level AI engineers, embedded developers, and hardware engineers who aim to implement AI models on low-power devices while minimizing energy consumption. By the end of this training, participants will be able to: - Understand the challenges associated with running AI on energy-efficient devices. - Optimize neural networks for low-power inference. - Employ quantization, pruning, and model compression techniques. - Deploy AI models on edge hardware with minimal power usage, ensuring alignment with public sector workflows for government.

This instructor-led, live training (online or onsite) is designed for intermediate-level AI developers, embedded engineers, and robotics engineers who aim to optimize and deploy AI models on NVIDIA Jetson platforms for edge applications in government settings. By the end of this training, participants will be able to: - Understand the fundamentals of edge AI and NVIDIA Jetson hardware. - Optimize AI models for deployment on edge devices. - Utilize TensorRT for accelerating deep learning inference. - Deploy AI models using JetPack SDK and ONNX Runtime for government applications.

This instructor-led, live training in [location] (online or onsite) is aimed at intermediate-level AI developers, machine learning engineers, and system architects who wish to optimize AI models for edge deployment for government applications. By the end of this training, participants will be able to: - Understand the challenges and requirements of deploying AI models on edge devices. - Apply model compression techniques to reduce the size and complexity of AI models. - Utilize quantization methods to enhance model efficiency on edge hardware. - Implement pruning and other optimization techniques to improve model performance. - Deploy optimized AI models on various edge devices for government use.

This instructor-led, live training (online or onsite) is designed for intermediate-level developers, data scientists, and technology enthusiasts who seek to acquire practical skills in deploying artificial intelligence (AI) models on edge devices for a variety of applications. By the end of this training, participants will be able to: - Understand the principles of Edge AI and its benefits. - Set up and configure the edge computing environment. - Develop, train, and optimize AI models for deployment on edge devices. - Implement practical AI solutions on edge devices. - Evaluate and enhance the performance of models deployed at the edge. - Address ethical and security considerations in Edge AI applications. This training is tailored to align with public sector workflows, governance, and accountability, ensuring that participants are well-equipped to apply these skills effectively for government projects.

This instructor-led, live training (online or onsite) is designed for intermediate-level finance professionals, fintech developers, and AI specialists who aim to implement Edge AI solutions in the financial sector. By the end of this training, participants will be able to: - Understand the role of Edge AI in financial services. - Implement fraud detection systems using Edge AI technologies. - Enhance customer service through AI-driven applications. - Apply Edge AI for risk management and informed decision-making. - Deploy and manage Edge AI solutions in financial environments, ensuring alignment with regulatory requirements for government and private sector entities.

This instructor-led, live training (available online or on-site) is designed for intermediate-level industrial engineers, manufacturing professionals, and AI developers who aim to implement Edge AI solutions in the context of industrial automation. By the end of this training, participants will be able to: - Understand the role of Edge AI in enhancing industrial automation. - Implement predictive maintenance solutions utilizing Edge AI. - Apply AI techniques to ensure quality control in manufacturing processes. - Optimize industrial processes through the use of Edge AI. - Deploy and manage Edge AI solutions effectively in industrial settings. This training is tailored to align with public sector workflows, governance, and accountability, ensuring that participants are equipped with the skills necessary for government applications.

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 wish to deploy AI-powered logic and control systems in manufacturing environments where speed, reliability, and offline operation are critical for government operations. By the end of this training, participants will be able to: - Understand the architecture and benefits of edge AI systems. - Build and optimize AI models for deployment on embedded devices. - Use 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 Edge AI applications for government.

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 deployment in government settings.
• Deploy TensorFlow Lite models on various edge devices used in public sector applications.
• Utilize tools and techniques for model conversion and optimization suitable for government workflows.
• Implement practical Edge AI applications using TensorFlow Lite to enhance public sector operations.

This instructor-led, live training (online or onsite) is designed for intermediate-level urban planners, civil engineers, and smart city project managers who seek to leverage Edge AI for government initiatives. By the end of this training, participants will be able to: - Comprehend the role of Edge AI in smart city infrastructures. - Implement Edge AI solutions for traffic management and surveillance. - Optimize urban resources using Edge AI technologies. - Integrate Edge AI with existing smart city systems. - Address ethical and regulatory considerations in smart city deployments for government.

This instructor-led, live training (conducted 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:

• Understand the security and privacy challenges associated with Edge AI.
• Implement best practices for securing edge devices and data.
• Develop strategies to mitigate security risks in Edge AI deployments.
• Address ethical considerations and ensure compliance with relevant regulations.
• Conduct thorough security assessments and audits for Edge AI applications.

This instructor-led, live training (online or onsite) is designed for intermediate-level robotics engineers, autonomous vehicle developers, and AI researchers who aim to harness Edge AI for innovative solutions in the field of autonomous systems. By the end of this training, participants will be able to: - Comprehend the role and advantages of Edge AI in autonomous systems. - Develop and deploy AI models for real-time processing on edge devices. - Implement Edge AI solutions for applications in autonomous vehicles, drones, and robotics. - Design and optimize control systems using Edge AI methodologies. - Address ethical and regulatory considerations relevant to autonomous AI applications, ensuring compliance with standards for government use.

This instructor-led, live training (available online or onsite) is designed for intermediate-level healthcare professionals, biomedical engineers, and artificial intelligence developers who aim to utilize Edge AI for innovative healthcare solutions. By the end of this training, participants will be able to: - Understand the role and benefits of Edge AI in healthcare. - Develop and deploy AI models on edge devices for healthcare applications. - Implement Edge AI solutions in wearable devices and diagnostic tools. - Design and deploy patient monitoring systems using Edge AI. - Address ethical and regulatory considerations in healthcare AI applications, ensuring alignment with public sector workflows, governance, and accountability for government.

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 designed for 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. - Convert and deploy AI models for on-device inference. - Optimize models for speed, size, and energy efficiency. - Integrate edge AI systems into robotic control architectures. - Evaluate performance and accuracy in real-world scenarios. **Format of the Course** - Interactive lecture and discussion. - Hands-on practice using TinyML and edge AI toolchains. - Practical exercises on embedded and robotic hardware platforms. **Course Customization Options** - To request a customized training for this course, please contact us to arrange.

The 6G and the Intelligent Edge is a forward-looking course 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 (online or onsite) is designed for intermediate-level IT architects who wish to understand and design next-generation distributed architectures leveraging 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 applications.
• Design distributed systems for ultra-low latency, high bandwidth, and autonomous operations in public sector environments.
• Integrate AI and data analytics at the edge to support intelligent decision-making for government services.
• Plan scalable, secure, and resilient 6G-ready edge infrastructures that meet government standards.
• Evaluate business and operational models enabled by 6G-edge convergence in a public sector context.

Format of the Course

• Interactive lectures and discussions tailored to government IT professionals.
• Case studies and applied architecture design exercises relevant to government projects.
• Hands-on simulation with optional edge or container tools, adapted for government use cases.

Course Customization Options

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

This instructor-led, live training (online or onsite) is designed for government and aimed at advanced-level 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:

• Explore 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.
• Optimize 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.

The Huawei Ascend CANN toolkit facilitates robust AI inference on edge devices such as the Ascend 310. CANN provides critical 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.

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

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

Format of the Course

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

Course Customization Options for Government

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

This instructor-led, live training (online or onsite) is designed for intermediate-level developers, system architects, and industry professionals who aim 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:

• Comprehend the core principles of Edge AI and its application in IoT systems.
• Establish and configure Edge AI environments for IoT devices.
• Create and deploy AI models on edge devices to support IoT applications.
• Execute real-time data processing and decision-making within IoT frameworks.
• Integrate Edge AI with a variety of IoT protocols and platforms.
• Address ethical considerations and best practices in the deployment of Edge AI for government and industry use cases.

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. - Set up a TinyML development environment suitable for IoT projects. - Develop and deploy machine learning models on low-power microcontrollers. - Implement predictive maintenance and anomaly detection using TinyML. - Optimize TinyML models for efficient power and memory usage, ensuring they meet the stringent requirements of government operations.

This instructor-led, live training (online or onsite) is designed for intermediate-level developers and IT professionals who wish to gain a comprehensive understanding of Edge AI, from conceptual foundations to practical implementation, including setup and deployment. By the end of this training, participants will be able to: - Understand the fundamental concepts of Edge AI. - Set up and configure Edge AI environments. - Develop, train, and optimize Edge AI models. - Deploy and manage Edge AI applications. - Integrate Edge AI with existing systems and workflows for government. - Address ethical considerations and best practices in Edge AI implementation.

This instructor-led, live training (online or onsite) is designed for intermediate-level embedded systems engineers and AI developers who are interested in deploying machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse for government applications. By the end of this training, participants will be able to: - Understand the fundamentals of TinyML and its benefits for edge AI applications. - Set up a development environment suitable for TinyML projects. - Train, optimize, and deploy AI models on low-power microcontrollers. - Utilize TensorFlow Lite and Edge Impulse to implement practical TinyML applications. - Optimize AI models to meet power efficiency and memory constraints.

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

This instructor-led, live training (available online or onsite) is tailored for intermediate-level developers who aim to construct and deploy AI models using the BANGPy framework and Neuware SDK on Cambricon MLU hardware.

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

• Set up and configure the BANGPy and Neuware development environments for government applications.
• Develop and optimize Python- and C++-based models for deployment on Cambricon MLUs.
• Deploy models to edge and data center devices running the Neuware runtime.
• Integrate machine learning workflows with MLU-specific acceleration features to enhance performance.

Format of the Course

• Interactive lecture and discussion sessions.
• Hands-on practice using BANGPy and Neuware for development and deployment tasks.
• Guided exercises focused on optimization, integration, and testing to ensure robust model performance.

Course Customization Options

• To request a customized training for this course based on specific Cambricon device models or use cases, please contact us to arrange.

This instructor-led, live training in [location] (online or onsite) is designed for government beginner-level developers and IT professionals who wish to gain a foundational understanding of Edge AI and its initial applications. By the end of this training, participants will be able to: - Comprehend the fundamental concepts and architecture of Edge AI. - Set up and configure Edge AI environments for government use. - Develop and deploy basic Edge AI applications. - Recognize and understand the use cases and benefits of Edge AI in public sector workflows.

This comprehensive course integrates the transformative power of artificial intelligence (AI) with the agility of edge computing. 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 is designed to equip professionals with the skills necessary to optimize AI performance for real-time processing and decision-making at the edge, ensuring alignment with public sector workflows and governance for government applications.