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

This instructor-led, live training in Sweden (online or onsite) is designed for intermediate-level telecom professionals, AI engineers, and IoT specialists interested in exploring how 5G networks enhance Edge AI applications.

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

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

This forward-thinking course delves into how 6G wireless technology converges with edge computing, IoT ecosystems, and AI-powered data processing to build intelligent, low-latency, and adaptive infrastructures.

Delivered via live, instructor-led sessions (available online or onsite), this program is designed for intermediate-level IT architects seeking to grasp and architect next-generation distributed systems by harnessing the combined power of 6G connectivity and intelligent edge platforms.

After completing this course, participants will be equipped to:

• Grasp how 6G will reshape edge computing and IoT architectures.
• Architect distributed systems capable of ultra-low latency, high bandwidth, and autonomous functions.
• Implement AI and data analytics at the edge to enable smart decision-making.
• Develop scalable, secure, and resilient 6G-ready edge infrastructures.
• Assess business and operational models that arise from the convergence of 6G and edge networks.

Course Format

• Interactive lectures and group discussions.
• Case studies and hands-on architecture design exercises.
• Practical simulations using optional edge or container-based tools.

Customization Options

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

This instructor-led, live training in Sweden (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 Sweden (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. It shifts reactive maintenance models toward proactive strategies, enhancing uptime, reducing costs, and extending asset lifespan.

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

Upon completing this training, participants will be able to:

• Distinguish predictive maintenance from reactive and preventive strategies.
• Gather and structure machine data for AI analysis.
• Utilize machine learning models to detect anomalies and predict failures.
• Deploy end-to-end workflows transforming sensor data into actionable insights.

Course Format

• Interactive lectures and discussions.
• Practical exercises and case studies.
• Live demonstrations and real-world data workflows.

Customization Options

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

Artificial Intelligence for Process Optimization involves utilizing machine learning and data analytics to boost efficiency, enhance product quality, and increase throughput within manufacturing environments.

This instructor-led training, available either online or onsite, is designed for manufacturing professionals with intermediate experience who aim to implement AI strategies to streamline operations, minimize downtime, and foster continuous improvement.

Upon completing this course, participants will be equipped to:

• Grasp AI concepts pertinent to manufacturing optimization.
• Gather and ready production data for analytical review.
• Utilize machine learning models to detect bottlenecks and forecast equipment failures.
• Visualize and interpret outcomes to facilitate evidence-based decision-making.

Course Format

• Engaging lectures coupled with interactive discussions.
• Numerous practical exercises and drills.
• Practical implementation within a live laboratory environment.

Customization Opportunities

• For tailored training options for this course, please reach out to us to arrange details.

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

This instructor-led training, available either online or onsite, is designed for quality professionals ranging from beginner to intermediate levels 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 generated by production lines.
• Apply machine learning and computer vision techniques to detect defects.
• Construct basic AI models for anomaly detection and yield forecasting.

Course Format

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

Customization Options

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

AI for Supply Chain and Manufacturing Logistics involves leveraging predictive analytics, machine learning, and automation to refine inventory management, route planning, and demand forecasting.

This instructor-led training, available online or on-site, is designed for intermediate-level supply chain professionals seeking to utilize AI-driven tools to improve logistics efficiency, enhance demand forecasting accuracy, and automate warehouse and transport operations.

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

• Grasp the application of AI across logistics and supply chain activities.
• Apply 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.

Course Format

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

Customization Options

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

Artificial Intelligence (AI) in smart factories involves applying AI technologies 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 who want to gain a strategic and practical introduction to leveraging AI within smart factory environments.

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

• Grasp the core principles of AI and machine learning.
• Identify key AI applications in manufacturing and automation.
• Explore how AI supports predictive maintenance, quality control, and process optimization.
• Evaluate the steps involved in launching AI-driven initiatives.

Format of the Course

• Interactive lecture and discussion.
• 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.

AI Use Case Implementation adopts a hands-on, project-centric methodology to apply machine learning, computer vision, and data analytics for resolving real-world industrial challenges using actual or simulated datasets.

This instructor-led, live training (available online or onsite) targets intermediate-level cross-functional teams seeking to collaboratively implement AI use cases that align with their operational objectives and gain practical experience with industrial data pipelines.

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

• Identifying and defining practical AI use cases within operations, quality assurance, or maintenance.
• Collaborating across various roles to develop machine learning solutions.
• Managing, cleaning, and analyzing diverse industrial datasets.
• Presenting a functional prototype of an AI-enabled solution based on a chosen use case.

Course Format

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

Customization Options

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

This instructor-led, live training in Sweden (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 processors optimized for both inference and training tasks in edge computing and data center environments.

This instructor-led live training, available online or onsite, is designed for intermediate-level developers who want 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:

• Set up and configure development environments for BANGPy and Neuware.
• Develop and optimize models written in Python and C++ for Cambricon MLUs.
• Deploy models to edge and data center devices operating on the Neuware runtime.
• Integrate machine learning workflows with MLU-specific acceleration capabilities.

Course Format

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

Customization Options

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

Digital Twins serve as virtual replicas of physical systems, augmented by real-time data feeds and AI-powered intelligence.

This instructor-led live training, available either online or onsite, is designed for intermediate-level professionals seeking to build, deploy, and optimize digital twin models leveraging real-time data and AI-driven insights.

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

• Grasp the architecture and key 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 analysis and anomaly detection.

Course Format

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

Customization Options

• For customized training requests, please contact us to make arrangements.

Artificial intelligence is revolutionizing industrial computer vision, enabling manufacturers and quality assurance teams to identify surface flaws, verify part compliance, and automate visual inspection workflows.

This instructor-led live training, available online or on-site, targets intermediate to advanced quality assurance teams, automation engineers, and developers aiming to design and implement AI-based computer vision systems for defect detection and inspection.

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

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

Course Format

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

Customization Options

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

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

This instructor-led training session, available both online and on-site, is designed for advanced robotics engineers, systems integrators, and automation specialists who aim to implement AI-driven perception, planning, and control within smart manufacturing settings.

Upon completing this training, participants will be able to:

• Comprehend and apply AI methodologies for robotic perception and sensor fusion.
• Create motion planning algorithms tailored for both collaborative and industrial robots.
• Implement learning-based control strategies to facilitate real-time decision-making.
• Seamlessly integrate intelligent robotic systems into smart factory workflows.

Course Format

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

Customization Options

• For bespoke training arrangements for this course, please get in touch with us.