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

This instructor-led, live training in Serbia (online or onsite) is aimed at intermediate-level telecom professionals, AI engineers, and IoT specialists who wish 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.

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

This instructor-led, live training (available online or onsite) is designed for intermediate-level IT architects who aim to comprehend and design next-generation distributed architectures that leverage the synergy of 6G connectivity and intelligent edge systems.

Upon completing this course, participants will be able to:

• Grasp how 6G will transform edge computing and IoT architectures.
• Design distributed systems capable of ultra-low latency, high bandwidth, and autonomous operations.
• Integrate AI and data analytics at the edge to enable intelligent decision-making.
• Plan scalable, secure, and resilient 6G-ready edge infrastructures.
• Evaluate the 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 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 Serbia (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 Serbia (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 utilizes machine learning and data analytics to anticipate equipment failures and optimize maintenance schedules. It shifts the paradigm from reactive to proactive strategies, enhancing equipment 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-based predictive maintenance solutions within industrial settings.

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

• Distinguish between predictive, reactive, and preventive maintenance strategies.
• Collect and structure machine data for AI-driven analysis.
• Apply machine learning models to identify anomalies and predict failures.
• Implement comprehensive workflows that transform sensor data into actionable insights.

Course Format

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

Customization Options

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

AI-driven process optimization leverages machine learning and data analytics to boost efficiency, quality, and throughput within manufacturing operations.

This instructor-led live training (available online or onsite) is designed for intermediate-level manufacturing professionals seeking to apply AI techniques to streamline operations, minimize downtime, and support continuous improvement initiatives.

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

• Comprehend AI concepts pertinent to manufacturing optimization.
• Collect and prepare production data for analysis.
• Apply machine learning models to identify bottlenecks and predict failures.
• Visualize and interpret results to support data-driven decisions.

Format of the Course

• Interactive lecture and discussion.
• Extensive 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.

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

This instructor-led live training (available 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 capable of:

• Grasping the application of AI in industrial quality control.
• Collecting and labeling image or sensor data from production lines.
• Applying machine learning and computer vision techniques to identify defects.
• Developing basic 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

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

The integration of Artificial Intelligence into Supply Chain and Manufacturing Logistics leverages predictive analytics, machine learning, and automation to refine inventory management, optimize routing, and enhance demand forecasting.

This instructor-led live training, available both online and on-site, is designed for intermediate-level supply chain professionals seeking to deploy AI-driven tools. The goal is to improve logistics performance, achieve precise demand forecasting, and automate warehouse and transportation operations.

Upon completing this training, participants will be equipped to:

• Grasp the application of AI across various logistics and supply chain functions.
• Utilize machine learning models for predicting demand and managing inventory.
• Analyze and optimize transport routes using AI methodologies.
• Automate decision-making processes within warehouses and fulfillment centers.

Training Format

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

Customization Options

• For tailored training on this subject, please reach out to us to make arrangements.

Artificial Intelligence in Smart Factories refers to the deployment of AI technologies to automate, oversee, and enhance industrial operations in real-time.

This instructor-led live training, available both online and 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:

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

Course Format

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

Customization Options

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

Implementing AI Use Cases utilizes a hands-on, project-based methodology to apply machine learning, computer vision, and data analytics to address real-world industrial challenges by leveraging actual or simulated datasets.

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

Upon completing this training, participants will be capable of:

• Identifying and defining practical AI use cases within operations, quality assurance, or maintenance.
• Collaborating across different roles to create machine learning solutions.
• Managing, cleaning, and analyzing varied industrial datasets.
• Demonstrating a functional prototype of an AI-powered solution based on a chosen use case.

Course Format

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

Customization Options

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

This instructor-led, live training in Serbia (online or onsite) is designed for advanced cybersecurity professionals, AI engineers, and IoT developers who wish to establish robust security protocols and resilience strategies for Edge AI systems.

Upon completing this training, participants will be capable of:

• Identifying security risks and vulnerabilities associated with Edge AI deployments.
• Deploying encryption and authentication methods to safeguard data.
• Architecting resilient Edge AI systems capable of withstanding cyber attacks.
• Executing secure AI model deployment strategies within edge environments.

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

This instructor-led live training (available online or on-site) targets intermediate developers looking to build and deploy AI models utilizing the BANGPy framework and Neuware SDK on Cambricon MLU hardware.

Upon completing this training, participants will be equipped to:

• Configure and set up 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 operating on the Neuware runtime.
• Integrate machine learning workflows with MLU-specific acceleration capabilities.

Course Format

• Interactive lectures and discussions.
• Practical, hands-on experience with 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 function as virtual counterparts to physical entities, augmented by live data feeds and artificial intelligence capabilities.

This instructor-led training, available online or in-person, targets intermediate-level professionals seeking to construct, implement, and optimize digital twin models through the application of real-time data and AI-driven analytics.

Upon completion of this program, participants will possess the ability to:

• Comprehend the architecture and constituent elements of digital twins.
• Employ simulation tools to construct models of intricate systems and environments.
• Incorporate real-time data streams into virtual representations.
• Utilize AI methodologies for predictive analysis and anomaly identification.

Course Format

• Engaging lectures and interactive discussions.
• Numerous exercises and practical applications.
• Practical implementation within a live-lab setting.

Customization Opportunities

• For bespoke training arrangements, please reach out to us.

The integration of artificial intelligence into industrial computer vision is revolutionizing the way manufacturers and quality assurance (QA) teams identify surface anomalies, verify component compliance, and automate visual inspection workflows.

This instructor-led training, available both online and onsite, targets intermediate to advanced QA professionals, automation engineers, and developers interested in designing and implementing AI-driven computer vision systems for defect detection and inspection.

Upon completion of this course, 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 techniques.
• Integrating real-time inspection pipelines with industrial cameras and hardware.
• Deploying and optimizing AI-enabled inspection systems for production environments.

Course Format

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

Customization Options

• For customized training arrangements, please contact us directly.

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

This instructor-led live training, available both online and onsite, is designed for robotics engineers, systems integrators, and automation leaders seeking to implement AI-driven perception, planning, and control solutions within smart manufacturing settings.

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

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

Training Format

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

Customization Options

• For personalized training requests, please get in touch with us to arrange your specific needs.