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

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

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

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

6G and the Intelligent Edge is a forward-looking course that explores the integration of 6G wireless technologies with edge computing, IoT ecosystems, and AI-driven data processing to support intelligent, low-latency, and adaptive infrastructures.

This instructor-led, live training (online or onsite) is aimed at intermediate-level IT architects who wish to understand and design next-generation distributed architectures leveraging the synergy of 6G connectivity and intelligent edge systems.

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

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

Format of the Course

• Interactive lectures and discussions.
• Case studies and applied architecture design exercises.
• Hands-on simulation 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 Greece (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 Greece (online or on-site) is designed for intermediate-level developers, data scientists, and tech enthusiasts seeking to gain practical skills in deploying AI models on edge devices for various applications.

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

• Comprehend the principles of Edge AI and its advantages.
• Establish and configure the edge computing environment.
• Develop, train, and optimize AI models for edge deployment.
• Implement practical AI solutions on edge devices.
• Assess and enhance the performance of models deployed on the edge.
• Address ethical and security considerations within Edge AI applications.

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

This instructor-led training session, available either online or on-site, is designed for intermediate-level professionals seeking to deploy AI-based predictive maintenance solutions within industrial settings.

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

• Distinguish predictive maintenance from reactive and preventive maintenance strategies.
• Gather and organize machine-generated data for AI-driven analysis.
• Utilize machine learning models to identify anomalies and forecast potential failures.
• Establish end-to-end workflows that transform raw sensor data into actionable insights.

Course Format

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

Customization Options

• For tailored training requirements, please contact us to arrange a customized program.

AI for Process Optimization involves leveraging machine learning and data analytics to boost efficiency, product quality, and throughput within manufacturing environments.

This instructor-led live training, available either online or at your location, is designed for intermediate-level manufacturing professionals seeking to apply AI techniques to streamline operations, minimize downtime, and drive continuous improvement initiatives.

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

• Grasping AI concepts pertinent to manufacturing optimization.
• Gathering and preparing production data for in-depth analysis.
• Utilizing machine learning models to pinpoint bottlenecks and forecast equipment failures.
• Visualizing and interpreting results to facilitate data-driven decision-making.

Course Format

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

Customization Options

• To arrange a customized training session for this course, please get in touch with us.

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

This instructor-led, live training (available online or onsite) is designed for beginner to intermediate-level quality professionals who wish to apply AI tools to automate inspections and enhance product quality in manufacturing environments.

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

• Understand the application of AI in industrial quality control.
• Collect and label image or sensor data from production lines.
• Utilize machine learning and computer vision to detect defects.
• Develop simple AI models for anomaly detection and yield forecasting.

Course Format

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

Course Customization Options

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

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

This instructor-led training session (available online or onsite) is designed for intermediate-level supply chain professionals seeking to leverage AI-driven tools to enhance logistics performance, achieve precise demand forecasts, and automate warehouse and transport operations.

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

• Comprehend the application of AI across various logistics and supply chain activities.
• Utilize machine learning models for accurate demand forecasting and inventory control.
• Analyze and optimize transport routes using AI-based techniques.
• Automate decision-making processes within warehouses and fulfillment workflows.

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 tailored training program for this course, please contact us to arrange.

AI in Smart Factories refers to the deployment of 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:

• Grasp the fundamental principles of AI and machine learning.
• Recognize primary AI use cases in manufacturing and automation.
• Examine how AI facilitates predictive maintenance, quality control, and process optimization.
• Assess the stages involved in initiating AI-driven projects.

Course Format

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

Customization Options

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

AI Use Case Implementation offers a hands-on, project-based methodology for applying 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 seeking to collaboratively implement AI use cases aligned with their operational goals and gain practical experience with industrial data pipelines.

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

• Identify and scope practical AI use cases from operations, quality, or maintenance.
• Collaborate across roles to develop machine learning solutions.
• Process, clean, and analyze diverse industrial datasets.
• Present a functional prototype of an AI-enabled solution based on a selected 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 Greece (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 designed to optimize both inference and training tasks for edge computing and datacenter environments.

This instructor-led live training session, available online or onsite, targets intermediate-level developers looking to create and deploy AI models on Cambricon MLU hardware using the BANGPy framework and the Neuware SDK.

Upon completing this course, participants will be able to:

• Configure and set up the development environments for BANGPy and Neuware.
• Develop and optimize models written in Python and C++ specifically for Cambricon MLUs.
• Deploy models to edge devices and datacenters 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 concentrating on optimization, integration, and testing.

Customization Options

• To arrange a customized training session tailored to your specific Cambricon device model or use case, please get in touch with us.

Digital Twins serve as virtual representations of physical entities, augmented by live data streams and artificial intelligence.

This instructor-led live training, available either online or in-person, targets intermediate-level professionals looking to create, deploy, and refine digital twin models utilizing real-time information and AI-derived insights.

Upon completion of this training, participants will gain the ability to:

• Grasp the architecture and key components of digital twins.
• Utilise simulation tools to model intricate systems and environments.
• Incorporate live data streams into virtual models.
• Apply AI methodologies for predictive analysis and anomaly identification.

Course Format

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

Customisation Options

• To request tailored training for this course, please get in touch with us to organise.

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

This instructor-led live training, available either online or on-site, targets intermediate to advanced quality assurance teams, automation engineers, and developers. The course focuses on designing and implementing computer vision systems for defect detection and inspection using AI techniques.

Upon completion, participants will be capable of:

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

Course Format

• Interactive lectures and group discussions.
• Extensive exercises and practical practice.
• Hands-on implementation in a live-lab setting.

Customization Options

• For customized training requests, 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 live training, available either online or onsite, is designed for advanced robotics engineers, systems integrators, and automation leaders who aim to implement AI-driven perception, planning, and control within smart manufacturing settings.

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

• Comprehend and apply AI methodologies for robotic perception and sensor fusion.
• Create motion planning algorithms for both collaborative and industrial robots.
• Implement learning-based control strategies to enable 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-lab environment.

Customization Options for the Course

• To arrange a tailored training session for this course, please get in touch with us.