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

This instructor-led live training in Bulgaria (online or onsite) targets intermediate-level telecom professionals, AI engineers, and IoT specialists interested in exploring the role of 5G networks in accelerating 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.

The '6G and the Intelligent Edge' course offers a forward-looking perspective on integrating 6G wireless technologies with edge computing, IoT ecosystems, and AI-driven data processing to enable intelligent, adaptive, and low-latency infrastructures.

This instructor-led training, available online or onsite, targets intermediate-level IT architects seeking to grasp and design next-generation distributed architectures that leverage the synergy between 6G connectivity and intelligent edge systems.

Upon completing this course, participants will be able to:

• Comprehend 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 to facilitate intelligent decision-making.
• Plan scalable, secure, and resilient 6G-ready edge infrastructures.
• Evaluate business and operational models enabled by the convergence of 6G and edge technologies.

Course Format

• 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 Bulgaria (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 conducted in Bulgaria (online or onsite) is tailored for intermediate-level developers, data scientists, and tech enthusiasts aiming to acquire practical skills in deploying AI models on edge devices for diverse applications.

By the conclusion 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 refine maintenance schedules. This approach shifts maintenance strategies from reactive to proactive, resulting in improved operational uptime, reduced costs, and extended asset lifespan.

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

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

• Distinguishing predictive maintenance from reactive and preventive maintenance strategies.
• Gathering and organizing machine data for AI analysis.
• Utilizing machine learning models to identify anomalies and forecast equipment failures.
• Establishing end-to-end 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

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

AI-driven process optimization involves utilizing machine learning and advanced data analytics to boost efficiency, product quality, and throughput within manufacturing environments.

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

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

• Grasp the core AI concepts applicable to manufacturing optimization.
• Gather and prepare production data for analytical purposes.
• Deploy machine learning models to pinpoint bottlenecks and forecast equipment failures.
• Visualize and interpret data outcomes to facilitate informed, data-backed decisions.

Course Format

• Engaging lectures and interactive discussions.
• Extensive exercises and practical practice sessions.
• Practical implementation within a live laboratory environment.

Customization Options

• For tailored training requests, please reach out to us to coordinate arrangements.

AI for Quality Control leverages 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 quality professionals at 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:

• 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 identify defects.
• Develop simple AI models for anomaly detection and yield forecasting.

Format of the Course

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

Course Customization Options

• For customized training on this course, please contact us to make arrangements.

The integration of AI into Supply Chain and Manufacturing Logistics involves leveraging predictive analytics, machine learning, and automation to streamline inventory management, optimize routing, and improve demand forecasting.

This instructor-led training, available both online and onsite, is designed for intermediate-level supply chain professionals seeking to utilize AI-driven tools to boost logistics performance, achieve precise demand forecasts, and automate warehouse and transport operations.

Upon completing this training, participants will be able to:

• Grasp the role of AI across various logistics and supply chain functions.
• Apply machine learning models for demand forecasting and inventory management.
• Utilize AI-based methods to analyze routes and optimize transportation.
• Implement automated decision-making within warehouses and fulfillment processes.

Course Format

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

Customization Options

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

AI in Smart Factories refers to the use of artificial intelligence to automate, monitor, and optimize industrial operations in real time.

This instructor-led, live training (online or onsite) is aimed at beginner-level decision-makers and technical leads who wish to gain a strategic and practical introduction to how AI can be leveraged in smart factory environments.

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

• Understand the core principles of AI and machine learning.
• Identify key AI use cases 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 offers a hands-on, project-based methodology for applying machine learning, computer vision, and data analytics to address real-world industrial challenges using real or simulated datasets.

This instructor-led live training (available online or onsite) is designed for intermediate-level cross-functional teams aiming to collaboratively implement AI use cases aligned with their operational goals while gaining 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 control, or maintenance.
• Collaborating across different 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 selected use case.

Course Format

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

Course Customization Options

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

This instructor-led, live training in Bulgaria (online or onsite) targets advanced 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:

• Grasp the 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.
• Utilize secure strategies for deploying AI models in edge environments.

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

This instructor-led live training, available online or on-site, is designed for intermediate-level developers looking 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:

• Configure and set up the development environments for BANGPy and Neuware.
• Develop and optimize Python- and C++-based models for Cambricon MLUs.
• Deploy models to edge devices and data centers running the Neuware runtime.
• Integrate ML workflows with acceleration features specific to MLU hardware.

Course Format

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

Customization Options

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

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 onsite) is designed for intermediate-level professionals aiming to create, deploy, and optimize digital twin models leveraging real-time data and AI-driven insights.

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

• Gaining insight into the architecture and key components of digital twins.
• Utilizing simulation tools to model complex systems and environments.
• Integrating real-time data streams into virtual models.
• Applying AI techniques for predictive behavior analysis and anomaly detection.

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 make arrangements.

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

This instructor-led live training, available either online or onsite, is designed for intermediate to advanced quality assurance teams, automation engineers, and developers who aim to design and implement computer vision systems for defect detection and inspection using AI techniques.

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

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

Course Format

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

Customization Options

• To request a tailored version of this course, please contact us to arrange details.

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

This instructor-led, live training (online or onsite) is aimed at advanced-level robotics engineers, systems integrators, and automation leads who wish to implement AI-driven perception, planning, and control in smart manufacturing environments.

By the end 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.