Edge AI in Industrial Automation Training Course

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.

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 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.

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 (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 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 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.

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 (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 (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 in US (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 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 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 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 (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.