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

This instructor-led live training in Slovakia (online or onsite) is designed for intermediate-level telecom professionals, AI engineers, and IoT specialists who want 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 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 Slovakia (online or onsite) is designed for advanced-level AI practitioners, researchers, and developers who aim to master the latest Edge AI advancements, optimize their 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 Edge AI model development and optimization.
• Apply state-of-the-art 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.
• Examine 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 Slovakia (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 advanced data analytics to anticipate equipment failures and optimize maintenance scheduling. This approach shifts traditional reactive models toward proactive strategies, resulting in improved operational uptime, reduced costs, and extended asset lifespan.

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

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

• Differentiating predictive maintenance from reactive and preventive maintenance approaches.
• Gathering and organizing machine data for AI-based analysis.
• Utilizing machine learning models to identify anomalies and forecast potential failures.
• Implementing comprehensive workflows that transform sensor data into actionable insights.

Course Format

• Engaging lectures and interactive discussions.
• Practical exercises and real-world case studies.
• Live demonstrations and hands-on data workflow practice.

Customization Options

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

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

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

Upon completing this training, participants will be capable of:

• Grasping AI concepts pertinent to manufacturing optimization.
• Gathering and preparing production data for analysis.
• Deploying machine learning models to detect bottlenecks and forecast failures.
• Visualizing and interpreting outcomes to facilitate data-driven decision-making.

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.

AI for Quality Control involves leveraging computer vision and machine learning techniques to detect defects, anomalies, and deviations within production processes.

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

Upon completing this training, participants will be able to:

• Grasp how AI is utilized in industrial quality control.
• Gather and annotate image or sensor data from production lines.
• Apply machine learning and computer vision methods to identify defects.
• Build simple AI models for anomaly detection and yield forecasting.

Course Format

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

Course Customization Options

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

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

This instructor-led live training, available online or onsite, is designed for intermediate-level supply chain professionals seeking to leverage AI-driven tools to boost logistics performance, achieve accurate demand forecasting, and automate warehouse and transportation operations.

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

• Grasp how AI is integrated into logistics and supply chain activities.
• Apply machine learning models for demand forecasting and inventory control.
• Analyze routes and optimize transport processes using AI techniques.
• Automate decision-making within warehouses and fulfillment workflows.

Course Format

• Interactive lectures and group discussions.
• Extensive exercises and practical application.
• 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 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 in smart factory environments.

Upon completing 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.
• Explore how AI facilitates predictive maintenance, quality control, and process optimization.
• Assess 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

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

The implementation of AI use cases employs a project-focused, practical methodology to apply machine learning, computer vision, and data analytics for addressing real-world industrial challenges using authentic or simulated datasets.

This live training, led by an instructor (available online or in-person), targets intermediate-level cross-functional teams seeking to collaboratively implement AI initiatives aligned with their operational objectives and acquire experience working with industrial data pipelines.

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

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

Course Format

• Interactive lectures and discussions.
• Collaborative exercises and project work in groups.
• Hands-on implementation within a live laboratory environment.

Customization Options

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

This instructor-led, live training in Slovakia (online or onsite) is designed for advanced-level 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 able to:

• Identify security risks and vulnerabilities associated with Edge AI deployments.
• Apply encryption and authentication techniques to protect data.
• 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 chips optimized for inference and training in edge and datacenter scenarios.

This instructor-led, live training (online or onsite) is aimed at 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 BANGPy and Neuware development environments.
• Develop and optimize Python- and C++-based models for Cambricon MLUs.
• Deploy models to edge and data center devices running Neuware runtime.
• Integrate ML workflows with MLU-specific acceleration features.

Format of the Course

• Interactive lecture and discussion.
• Hands-on use of BANGPy and Neuware for development and deployment.
• Guided exercises focused on optimization, integration, and testing.

Course Customization Options

• To request a customized training for this course based on your Cambricon device model or use case, please contact us to arrange.

Digital Twins serve as virtual copies of physical assets, augmented with real-time data and AI-powered analytics.

This guided, live training session (available online or in-person) is designed for mid-level professionals seeking to create, implement, and refine digital twin models by leveraging real-time information and AI-driven insights.

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

• Grasping the architecture and key elements of digital twins.
• Employing simulation tools to model complex systems and environments.
• Integrating real-time data flows into virtual representations.
• Applying AI methods for predictive analysis and anomaly identification.

Course Format

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

Customization Opportunities

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

Industrial computer vision powered by AI is revolutionizing the way manufacturers and quality assurance teams identify surface defects, verify part compliance, and automate visual inspection workflows.

This instructor-led, live training session (available online or onsite) is designed for QA teams, automation engineers, and developers at an intermediate to advanced level who aim to design and implement computer vision systems for defect detection and inspection using AI methodologies.

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

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

Format of the Course

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

Course Customization Options

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

Smart Robotics represents the fusion of artificial intelligence with 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 seeking 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 techniques 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.
• Integrate intelligent robotic systems into smart factory operational workflows.

Format of the Course

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

Course Customization Options

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