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

This instructor-led, live training in Taiwan (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.

6G and the Intelligent Edge is an advanced course that delves into the integration of 6G wireless technologies with edge computing, IoT ecosystems, and AI-driven data processing. This course focuses on creating intelligent, low-latency, and adaptive infrastructures.

This instructor-led, live training (available both online and onsite) is designed for intermediate-level IT architects who want to gain a deep understanding of how to design next-generation distributed architectures that leverage the synergy between 6G connectivity and intelligent edge systems.

Upon completing this course, participants will be able to:

• Understand how 6G technology will revolutionize edge computing and IoT architectures.
• Design distributed systems that support 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.
• Evaluate new business and operational models made possible by the convergence of 6G and edge computing.

Format of the Course

• Interactive lectures and discussions.
• Case studies and practical architecture design exercises.
• Hands-on simulations with optional edge or container tools.

Course Customization Options

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

This instructor-led, live training in Taiwan (online or onsite) is 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 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 in Taiwan (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-powered predictive maintenance leverages machine learning and data analytics to predict equipment failures and optimize maintenance schedules. It transforms reactive maintenance practices into proactive strategies, enhancing uptime, reducing costs, and extending the life of assets.

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

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

• Understand the differences between predictive, reactive, and preventive maintenance strategies.
• Gather and organize machine data for AI analysis.
• Use machine learning models to identify anomalies and forecast failures.
• Implement comprehensive workflows from sensor data collection to actionable insights.

Format of the Course

• Interactive lectures and discussions.
• Hands-on exercises and case studies.
• Live demonstrations and practical data workflows.

Course Customization Options

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

AI for Process Optimization involves the use of machine learning and data analytics to enhance efficiency, quality, and throughput in manufacturing processes.

This instructor-led, live training (available online or on-site) is designed for intermediate-level manufacturing professionals who aim to apply AI techniques to streamline operations, minimize downtime, and support continuous improvement initiatives.

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

• Grasp AI concepts relevant to manufacturing optimization.
• Gather and prepare production data for analysis.
• Implement machine learning models to identify bottlenecks and predict failures.
• Visualize and interpret results to facilitate data-driven decisions.

Format of the Course

• Interactive lecture and discussion sessions.
• Extensive exercises and practical activities.
• 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 for Quality Control involves the application of computer vision and machine learning methods to detect defects, anomalies, and deviations in manufacturing processes.

This instructor-led, live training (available both online and onsite) is designed for quality professionals at beginner to intermediate levels who aim to leverage AI tools for automating inspections and enhancing product quality in production environments.

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

• Gain an understanding of how AI is utilized in industrial quality control.
• Collect and label image or sensor data from manufacturing lines.
• Employ machine learning and computer vision techniques to identify defects.
• Develop simple AI models for anomaly detection and yield forecasting.

Format of the Course

• Interactive lectures and discussions.
• Numerous exercises and practical 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.

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

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

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

• Understand how AI is utilized across various logistics and supply chain activities.
• Apply machine learning models for demand forecasting and inventory control.
• Analyze routes and optimize transportation using AI-driven techniques.
• Automate decision-making processes in warehouses and fulfillment centers.

Format of the Course

• Interactive lectures and discussions.
• Numerous exercises and practical 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.

AI in Smart Factories involves the application of artificial intelligence to automate, monitor, and optimize industrial processes in real-time.

This instructor-led, live training (available online or on-site) is designed for beginner-level decision-makers and technical leads who want to gain a strategic and practical understanding of how AI can be utilized in smart factory settings.

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

• Grasp the fundamental principles of AI and machine learning.
• Recognize key applications of AI in manufacturing and automation.
• Examine how AI enhances 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 activities.
• Strategic frameworks and implementation guidance.

Course Customization Options

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

AI Use Case Implementation is a practical, project-oriented approach that applies machine learning, computer vision, and data analytics to address real-world industrial challenges using actual or simulated datasets.

This instructor-led, live training (available online or onsite) is designed for intermediate-level cross-functional teams who aim to collaboratively implement AI use cases aligned with their operational goals and gain hands-on experience working with industrial data pipelines.

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

• Identify and define practical AI use cases from operations, quality, or maintenance.
• Collaborate effectively across different roles to develop machine learning solutions.
• Manage, clean, and analyze a variety of industrial datasets.
• Showcase a working prototype of an AI-enabled solution based on a chosen use case.

Format of the Course

• Interactive lectures and discussions.
• Group-based exercises and project work.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• For customized training options for this course, please contact us to arrange.

This instructor-led, live training in Taiwan (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.

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

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

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

This instructor-led, live training (available online or on-site) is tailored for intermediate-level developers who wish to build 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 development environments for BANGPy and Neuware.
• Create and optimize Python- and C++-based models for use with Cambricon MLUs.
• Deploy these models to edge and data center devices that run the Neuware runtime.
• Integrate machine learning workflows with specific acceleration features provided by MLUs.

Format of the Course

• Interactive lectures and discussions.
• Practical, hands-on use of BANGPy and Neuware for development and deployment tasks.
• Guided exercises focusing on optimization, integration, and testing processes.

Course Customization Options

• To request a customized training session based on your specific Cambricon device model or use case, please contact us to arrange the details.

Digital Twins are virtual replicas of physical systems enhanced with real-time data and AI-driven intelligence.

This instructor-led, live training (conducted online or on-site) is designed for intermediate-level professionals who aim to create, deploy, and optimize digital twin models using real-time data and AI-based insights.

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

• Comprehend the architecture and components of digital twins.
• Utilize simulation tools to model complex systems and environments.
• Integrate real-time data streams into virtual models.
• Apply AI techniques for predictive behavior and anomaly detection.

Format of the Course

• Interactive lectures and discussions.
• Numerous 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.

Industrial computer vision enhanced by AI is revolutionizing the way manufacturers and quality assurance (QA) teams identify surface defects, verify part compliance, and automate visual inspection processes.

This instructor-led, live training (available both online and on-site) is designed for intermediate to advanced QA teams, automation engineers, and developers who aim to design and implement computer vision systems for defect detection and inspection using AI techniques.

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

• Grasp the architecture and components of industrial vision systems.
• Develop AI models for visual defect detection using deep learning.
• Integrate real-time inspection pipelines with industrial cameras and devices.
• Deploy and optimize AI-driven inspection systems for production environments.

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.

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

This instructor-led, live training (available online or onsite) is designed for advanced-level robotics engineers, systems integrators, and automation leaders who are looking to implement AI-driven perception, planning, and control in smart manufacturing environments.

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

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

Format of the Course

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

Course Customization Options

• For customized training options for this course, please contact us to arrange.