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

This instructor-led, live training in Norway (online or onsite) is designed for intermediate-level telecom professionals, AI engineers, and IoT specialists keen on understanding how 5G networks accelerate Edge AI applications.

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

• Grasp the fundamentals of 5G technology and its influence on Edge AI.
• Deploy AI models tailored for low-latency applications within 5G environments.
• Implement real-time decision-making systems leveraging Edge AI and 5G connectivity.
• Optimize AI workloads to ensure efficient performance on edge devices.

6G and the Intelligent Edge is an innovative course that investigates the convergence of 6G wireless technologies, edge computing, IoT ecosystems, and AI-powered data processing to build intelligent, adaptive, and low-latency infrastructures.

This instructor-led live training (available online or onsite) targets intermediate-level IT architects looking to master and design next-generation distributed architectures that leverage the combined strengths of 6G connectivity and intelligent edge systems.

After completing this course, participants will be able to:

• Grasp how 6G will revolutionize edge computing and IoT architectures.
• Develop distributed systems for autonomous operations, high bandwidth, and ultra-low latency.
• Implement AI and data analytics at the edge to enable intelligent decision-making.
• Architect scalable, secure, and resilient edge infrastructures prepared for 6G.
• Assess the business and operational models facilitated by the convergence of 6G and edge computing.

Course Format

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

Course Customization Options

• For customized training arrangements, please contact us.

This instructor-led, live training in Norway (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 live, instructor-led training in Norway (online or onsite) is designed for intermediate developers, data scientists, and technology enthusiasts looking to gain practical skills in deploying AI models on edge devices for various applications.

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

• Grasp the core principles of Edge AI and its advantages.
• Establish and configure the necessary edge computing infrastructure.
• Create, train, and refine AI models tailored for edge deployment.
• Deploy practical AI solutions onto edge hardware.
• Assess and enhance the performance of models running on the edge.
• Navigate ethical and security aspects inherent to Edge AI applications.

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

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

Upon completion, participants will be able to:

• Differentiate predictive maintenance from reactive and preventive maintenance strategies.
• Gather and organize machine data for AI analysis.
• Utilize machine learning models to identify anomalies and predict failures.
• Deploy comprehensive workflows transforming sensor data into actionable insights.

Course Format

• Interactive lectures and discussions.
• Practical exercises and case studies.
• Live demonstrations and hands-on data workflows.

Customization Options

• For tailored training on this topic, please contact us to arrange a session.

AI-driven process optimization applies machine learning and data analytics to boost efficiency, quality, and throughput in manufacturing operations.

This instructor-led live training, available online or onsite, targets intermediate-level manufacturing professionals aiming to leverage AI techniques to streamline operations, minimize downtime, and support continuous improvement efforts.

Upon completing this training, participants will be able to:

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

Course Format

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

Course Customization Options

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

AI-driven Quality Control leverages computer vision and machine learning to detect defects, anomalies, and deviations within production processes.

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

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

• Comprehend the application of AI in industrial quality control.
• Gather and annotate image or sensor data from production lines.
• Employ machine learning and computer vision technologies to identify defects.
• Create basic AI models for anomaly detection and yield forecasting.

Course Format

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

Customization Options

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

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

This instructor-led live training (available online or onsite) targets intermediate-level supply chain professionals looking to apply AI-powered tools to boost logistics performance, improve demand forecasting accuracy, and automate warehouse and transport operations.

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

• Gain insight into how AI is utilized across logistics and supply chain activities.
• Employ machine learning models for demand forecasting and inventory control.
• Evaluate routes and enhance transport efficiency using AI-based methods.
• Automate decision-making processes in warehouses and fulfillment operations.

Format of the Course

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

Course Customization Options

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

Artificial Intelligence in Smart Factories involves applying AI technologies to automate, monitor, and optimize industrial operations in real time.

This instructor-led live training, available both online and 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 completing this training, participants will be able to:

• Grasp the fundamental principles of AI and machine learning.
• Identify key AI applications in manufacturing and automation.
• Explore how AI facilitates predictive maintenance, quality control, and process optimization.
• Evaluate the steps required to launch AI-driven initiatives.

Course Format

• 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.

This course offers a hands-on, project-based methodology for leveraging machine learning, computer vision, and data analytics to address tangible industrial challenges, utilizing real or simulated datasets.

Designed as an instructor-led, live session (available online or on-site), the training targets intermediate-level cross-functional teams eager to collaboratively deploy AI solutions that align with their operational objectives while gaining practical experience with industrial data pipelines.

Upon completion, participants will be equipped to:

• Identify and define actionable AI use cases within operations, quality assurance, or maintenance.
• Collaborate effectively across different roles to engineer machine learning solutions.
• Manage, clean, and analyze varied industrial data sets.
• Deliver a functional prototype of an AI-driven solution based on a chosen use case.

Course Format

• Interactive lectures and discussions.
• Collaborative group exercises and project work.
• Practical implementation within a live laboratory environment.

Customization Options

• For information on tailoring this course to your specific needs, please contact us.

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

Upon completion of this training, participants will be capable of:

• Identifying security risks and vulnerabilities associated with Edge AI deployments.
• Applying encryption and authentication techniques to protect data.
• Architecting resilient Edge AI systems capable of withstanding cyber threats.
• Executing secure AI model deployment strategies within edge environments.

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

This instructor-led, live training session (available online or on-site) targets intermediate-level developers who aim to construct and deploy AI models utilizing the BANGPy framework and Neuware SDK on Cambricon MLU hardware.

Upon completing this training, participants will be able to:

• Establish and configure development environments for BANGPy and Neuware.
• Construct and optimize Python and C++ based models for Cambricon MLUs.
• Deploy models to edge and data center devices operating with the Neuware runtime.
• Integrate machine learning workflows with acceleration features specific to MLUs.

Course Format

• Interactive lectures and discussions.
• Practical application of BANGPy and Neuware for development and deployment.
• Guided exercises emphasizing optimization, integration, and testing.

Course Customization Options

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

Digital Twins are virtual representations of physical systems, augmented by real-time data streams and intelligent analysis powered by AI.

This guided live training, available either online or on-site, is designed for intermediate-level professionals who aim to construct, implement, and refine digital twin models leveraging real-time information and AI-driven insights.

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

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

Course Format

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

Course Customization Options

• For inquiries regarding customized training for this course, please reach out to us to make arrangements.

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

This instructor-led live training, available both online and onsite, is designed for intermediate to advanced QA teams, automation engineers, and developers looking to design and implement computer vision systems for defect detection and inspection utilizing AI methodologies.

Upon completing this training, participants will be able to:

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

Course Format

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

Customization Options

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

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

This instructor-led live training, available both online and onsite, is designed for advanced robotics engineers, systems integrators, and automation leads who want 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.