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

This instructor-led, live training in Czech Republic (online or onsite) is designed for intermediate-level professionals in telecommunications, AI engineering, and IoT who want to explore how 5G networks accelerate Edge AI applications.

Upon completing this training, participants will be capable of:

• Grasping the core principles of 5G technology and its influence on Edge AI.
• Deploying AI models specifically optimized for low-latency requirements within 5G infrastructures.
• Building real-time decision-making systems that leverage Edge AI and 5G connectivity.
• Optimizing AI workloads to ensure efficient operation on edge devices.

6G and the Intelligent Edge is a forward-thinking course that investigates the convergence of 6G wireless technologies with edge computing, IoT ecosystems, and AI-powered data processing to enable intelligent, low-latency, and adaptive infrastructures.

This instructor-led, live training (available online or onsite) targets intermediate-level IT architects looking to understand and design next-generation distributed architectures by leveraging the synergy between 6G connectivity and intelligent edge systems.

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

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

Format of the Course

• Interactive lectures and discussions.
• Case studies and applied architecture design exercises.
• Hands-on simulation using 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 Czech Republic (online or onsite) targets advanced-level AI practitioners, researchers, and developers aiming to master recent advancements in Edge AI, optimize their AI models for edge deployment, and explore specialized applications across diverse industries.

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

• Investigate advanced techniques for developing and optimizing Edge AI models.
• Apply modern 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.
• Discover 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 Czech Republic (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 advanced data analytics to anticipate equipment failures and optimize maintenance schedules. This approach shifts organizations from reactive maintenance models to proactive strategies, resulting in improved uptime, reduced costs, and extended 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 completion of this training, participants will be equipped to:

• Distinguish predictive maintenance from reactive and preventive maintenance strategies.
• Gather and organize machine data for AI-driven analysis.
• Utilize machine learning models to identify anomalies and forecast failures.
• Establish complete workflows ranging from sensor data collection to actionable insights.

Course Format

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

Customization Options

• For personalized training requests, please reach out to us to make arrangements.

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

This instructor-led live training (available online or on-site) targets intermediate-level manufacturing professionals seeking to use AI techniques to streamline operations, minimize downtime, and drive continuous improvement.

Upon completion, participants will be able to:

• Grasp AI concepts pertinent to manufacturing optimization.
• Gather and prepare production data for analysis.
• Utilize machine learning models to spot bottlenecks and forecast failures.
• Interpret and visualize results to facilitate data-driven decision-making.

Course Format

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

Customization Options

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

AI-driven quality control utilizes computer vision and machine learning to detect defects, anomalies, and process deviations within manufacturing workflows.

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

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

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

Course Format

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

Customization Options

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

AI in Supply Chain and Manufacturing Logistics leverages predictive analytics, machine learning, and automation to optimize inventory management, routing efficiency, and demand forecasting.

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

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

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

Course Format

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

Customization Options

• For a customized training session, please contact us to arrange details.

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 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 completing this training, participants will be able to:

• Grasp the fundamental principles of AI and machine learning.
• Recognize key AI use cases in manufacturing and automation sectors.
• Explore how AI facilitates predictive maintenance, quality control, and process optimization.
• Assess the necessary steps for initiating AI-driven projects.

Course Format

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

Course Customization Options

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

This course adopts a project-based methodology to teach the application of machine learning, computer vision, and data analytics for addressing real-world industrial challenges using authentic or simulated datasets.

Delivered by an instructor via online or onsite sessions, this training targets intermediate-level, cross-functional teams seeking to collaboratively implement AI initiatives that align with their operational objectives. Participants will gain practical experience working with industrial data pipelines.

Upon completion, participants will be able to:

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

Course Format

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

Customization Options

• For customized training arrangements, please contact us.

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

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

• Grasp the security risks and vulnerabilities associated with Edge AI deployments.
• Apply encryption and authentication techniques to safeguard data.
• Architect resilient Edge AI systems capable of withstanding cyber threats.
• Deploy AI models securely within edge environments.

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

This instructor-led live training session (available online or onsite) targets intermediate-level developers who want to build and deploy AI models leveraging the BANGPy framework and Neuware SDK on Cambricon MLU hardware.

Upon completing this training, participants will be able to:

• Set up and configure the development environment for BANGPy and Neuware.
• Develop and optimize Python- and C++-based models specifically for Cambricon MLUs.
• Deploy models to edge devices and data centers running the Neuware runtime.
• Integrate machine learning workflows with MLU-specific acceleration features.

Course Format

• Interactive lectures and discussions.
• Practical, hands-on development and deployment using BANGPy and Neuware.
• Guided exercises concentrating on optimization, integration, and testing.

Customization Options

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

Digital Twins serve as virtual representations of physical systems, augmented by real-time information and AI-powered analytics.

This instructor-led live training (available online or on-site) targets intermediate-level professionals seeking to construct, implement, and optimize digital twin models utilizing real-time data and AI-based insights.

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

• Grasping the architecture and constituent elements of digital twins.
• Employing simulation tools to model intricate systems and environments.
• Integrating real-time data streams into virtual models.
• Applying AI techniques for predicting behavior and detecting anomalies.

Course Format

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

Course Customization Options

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

The integration of AI into industrial computer vision is revolutionizing the way manufacturers and quality assurance teams identify surface defects, verify component conformity, and automate visual inspection workflows.

This instructor-led live training, available both online and onsite, is designed for intermediate to advanced quality assurance teams, automation engineers, and developers looking to design and implement computer vision systems for defect detection and inspection using artificial intelligence.

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

• Understanding the architecture and key components of industrial vision systems.
• Developing AI models for visual defect detection utilizing deep learning techniques.
• 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 discussions.
• Extensive exercises and practical application.
• Hands-on implementation in a live-lab environment.

Customization Options

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

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

This instructor-led live training, available either online or on-site, is designed for advanced robotics engineers, systems integrators, and automation leads aiming to implement AI-driven perception, planning, and control within smart manufacturing settings.

Upon completing 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.
• Deploy 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.
• Numerous exercises and practice sessions.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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