Edge AI for Manufacturing: Real-Time Intelligence at the Device Level Training Course

This instructor-led, live training in South Korea (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.

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 forward-looking course examines the convergence of 6G wireless technologies with edge computing, IoT ecosystems, and AI-powered data processing to build intelligent, adaptive, and low-latency infrastructures.

Designed for intermediate-level IT architects, this instructor-led live training (available online or onsite) helps participants understand and design next-generation distributed architectures that leverage the synergy between 6G connectivity and intelligent edge systems.

Upon completion, participants will be able to:

• Grasp how 6G will reshape edge computing and IoT architectures.
• Design distributed systems optimized for ultra-low latency, high bandwidth, and autonomous operations.
• Integrate AI and data analytics at the edge to enable intelligent decision-making.
• Plan scalable, secure, and resilient infrastructures ready for 6G adoption.
• Evaluate the business and operational models facilitated by the 6G-edge convergence.

Course Format

• Interactive lectures and guided discussions.
• Case studies and applied architecture design exercises.
• Hands-on simulation using optional edge or container tools.

Customization Options

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

This instructor-led, live training in South Korea (online or onsite) is designed for 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:

• Investigate advanced techniques in the development and optimization of Edge AI models.
• Apply innovative strategies for deploying AI models on edge devices.
• Leverage specialized tools and frameworks for advanced Edge AI applications.
• Enhance the performance and efficiency of Edge AI solutions.
• Discover innovative use cases and emerging trends in Edge AI.
• Tackle advanced ethical and security challenges in Edge AI deployments.

This instructor-led, live training in South Korea (online or onsite) is aimed at intermediate-level developers, data scientists, and tech enthusiasts who wish to gain practical skills in deploying 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.
• Set up and configure the edge computing environment.
• Develop, train, and optimize AI models for edge deployment.
• Implement practical AI solutions on edge devices.
• Evaluate and improve the performance of edge-deployed models.
• Address ethical and security considerations in Edge AI applications.

AI-driven predictive maintenance leverages machine learning and data analytics to anticipate equipment failures and optimize maintenance schedules. This approach shifts organizations from reactive maintenance models to proactive strategies, resulting in improved uptime, reduced costs, and extended asset lifespan.

This instructor-led live training (available online or onsite) is designed for intermediate-level professionals looking to implement AI-driven predictive maintenance solutions within industrial settings.

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

• Differentiate predictive maintenance from reactive and preventive strategies.
• Gather and organize machine data for AI-based analysis.
• Utilize machine learning models to identify anomalies and forecast failures.
• Deploy end-to-end workflows that transform sensor data into actionable insights.

Course Format

• Interactive lectures and discussions.
• Practical exercises and case study analysis.
• Live demonstrations and real-world data workflow practice.

Customization Options

• For customized training on this course, please reach out to us to make arrangements.

AI-driven process optimization leverages machine learning and advanced data analytics to boost efficiency, enhance product quality, and increase throughput in manufacturing environments.

This instructor-led training, available both online and onsite, is designed for intermediate-level manufacturing professionals seeking to apply AI techniques to streamline operations, minimize downtime, and advance continuous improvement efforts.

Upon completion of this program, participants will be equipped to:

• Grasp AI concepts specifically applicable to manufacturing optimization.
• Gather and prepare production data for in-depth analysis.
• Utilize machine learning models to pinpoint bottlenecks and forecast equipment failures.
• Visualize and interpret analytical results to facilitate data-driven decision-making.

Course Format

• Interactive lectures and group discussions.
• Extensive exercises and practical practice sessions.
• Hands-on implementation within a live-lab environment.

Customization Options

• For customized training tailored to your organization's needs, please contact us to arrange a session.

AI-driven quality control leverages computer vision and machine learning to identify defects, anomalies, and process deviations within production environments.

This instructor-led live training, available online or onsite, is designed for quality professionals at beginner to intermediate levels who aim to utilize AI tools to automate inspections and enhance product quality in manufacturing settings.

Upon completing this training, participants will be capable of:

• Grasping the application of AI in industrial quality control.
• Collecting and labeling image or sensor data generated by production lines.
• Employing machine learning and computer vision to detect defects.
• Developing basic AI models for anomaly detection and yield forecasting.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical sessions.
• Hands-on implementation within a live laboratory environment.

Customization Options

• For customized training requests, please contact us to arrange your session.

AI in Supply Chain and Manufacturing Logistics involves utilizing predictive analytics, machine learning, and automation to optimize inventory management, routing, and demand forecasting.

This instructor-led, live training (online or onsite) is designed for intermediate-level supply chain professionals who want to leverage AI-driven tools to improve logistics performance, accurately forecast demand, and automate warehouse and transport operations.

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

• Understand how AI is applied across logistics and supply chain activities.
• Use machine learning models for demand forecasting and inventory control.
• Analyze routes and optimize transport using AI-based techniques.
• Automate decision-making in warehouses and fulfillment processes.

Format of the Course

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

Course Customization Options

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

AI in Smart Factories involves applying artificial intelligence to automate, monitor, and optimize industrial operations in real time.

This instructor-led live training (available online or onsite) is designed for beginner-level decision-makers and technical leads seeking a strategic and practical introduction to leveraging AI within smart factory environments.

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

• Comprehend the fundamental principles of AI and machine learning.
• Recognize key AI use cases in manufacturing and automation.
• Examine how AI facilitates predictive maintenance, quality control, and process optimization.
• Assess the steps required to initiate AI-driven projects.

Format of the Course

• Interactive lectures and discussions.
• Real-world case studies and group exercises.
• Strategic frameworks and implementation guidance.

Course Customization Options

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

Implementing AI Use Cases employs a hands-on, project-oriented methodology to apply machine learning, computer vision, and data analytics techniques to resolve real-world industrial challenges using authentic or simulated datasets.

This instructor-led, live training session (available online or on-site) is designed for intermediate-level cross-functional teams aiming to collaboratively develop AI applications that align with their operational objectives and gain practical experience with industrial data pipelines.

Upon completion of this training, participants will be equipped to:

• Identify and define practical AI use cases across operations, quality control, or maintenance functions.
• Collaborate across various roles to engineer machine learning solutions.
• Manage, cleanse, and analyze a wide range of industrial datasets.
• Present a functional prototype of an AI-driven solution tailored to a chosen use case.

Course Format

• Engaging lectures and interactive discussions.
• Collaborative group exercises and project development.
• Practical implementation within a live laboratory environment.

Customization Options

• For tailored training requests for this course, please reach out to us to make arrangements.

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

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

• Comprehend the security risks and vulnerabilities associated with Edge AI deployments.
• Apply encryption and authentication techniques to ensure data protection.
• Design resilient Edge AI architectures capable of withstanding cyber threats.
• Execute secure AI model deployment strategies within edge environments.

Cambricon MLUs (Machine Learning Units) are specialized AI accelerators engineered for optimized inference and training across both edge computing and datacenter environments.

This instructor-led live training, available online or onsite, is designed for intermediate developers seeking to build and deploy AI models utilizing the BANGPy framework and Neuware SDK on Cambricon MLU hardware.

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

• Install and configure the BANGPy and Neuware development environments.
• Develop and optimize Python- and C++-based models specifically for Cambricon MLUs.
• Deploy models to edge and datacenter devices operating on the Neuware runtime.
• Integrate machine learning workflows with MLU-specific acceleration capabilities.

Course Format

• Interactive lectures and discussions.
• Hands-on practice with BANGPy and Neuware for development and deployment.
• Guided exercises focusing on optimization, integration, and testing.

Customization Options

• To arrange customized training tailored to your specific Cambricon device model or use case, please contact us.

Digital Twins function as virtual mirrors of physical entities, amplified by real-time information and artificial intelligence capabilities.

This trainer-led, live training session (available online or at a facility) targets intermediate professionals seeking to construct, implement, and enhance digital twin models through the utilization of real-time information and AI-driven insights.

Upon completing this training, participants will possess the ability to:

• Grasp the architecture and constituent parts of digital twins.
• Employ simulation software to model intricate systems and settings.
• Incorporate real-time data flows into virtual models.
• Apply AI methodologies for predicting behaviors and detecting anomalies.

Course Format

• Engaging lectures and discussions.
• Abundant exercises and practical application.
• Practical implementation within a live-lab setting.

Course Customization Choices

• To arrange a customized training for this course, please reach out to us.

The integration of artificial intelligence into industrial computer vision is revolutionizing the way manufacturing and quality assurance (QA) departments identify surface defects, verify part compliance, and automate visual inspection workflows.

This instructor-led live training, available both online and onsite, targets intermediate to advanced QA teams, automation engineers, and developers who aim to design and deploy computer vision systems for defect detection and inspection using AI methodologies.

Upon completing this training, participants will be capable of:

• Grasping the architecture and key components of industrial vision systems.
• Constructing AI models for visual defect detection utilizing deep learning.
• Integrating real-time inspection pipelines with industrial cameras and hardware.
• Deploying and optimizing AI-powered inspection systems within production environments.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical practice.
• Hands-on implementation within a live lab environment.

Customization Options

• To request customized training for this course, please contact us to make arrangements.

Smart Robotics involves integrating artificial intelligence into robotic systems to enhance perception, decision-making capabilities, and autonomous control.

This instructor-led live training, available online or onsite, targets advanced-level robotics engineers, systems integrators, and automation leaders seeking to implement AI-driven perception, planning, and control within smart manufacturing settings.

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

• Understand and apply AI techniques for robotic perception and sensor fusion.
• Develop motion planning algorithms for collaborative and industrial robots.
• Deploy learning-based control strategies for real-time decision making.
• Integrate intelligent robotic systems into smart factory workflows.

Format of the Course

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

Course Customization Options

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