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

This instructor-led, live training in Uzbekistan (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.

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 Uzbekistan (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 Uzbekistan (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 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 lifespan.

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

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

• Differentiating predictive maintenance from reactive and preventive maintenance strategies.
• Gathering and organizing machine data for AI-based analysis.
• Utilizing machine learning models to identify anomalies and forecast equipment failures.
• Implementing comprehensive workflows that transform 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.

Course Customization Options

• For customized training arrangements, please contact us to discuss your specific needs.

AI for Process Optimization refers to the application of machine learning and data analytics to improve efficiency, quality, and throughput in manufacturing operations.

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

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

• Understand AI concepts relevant 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 decision-making.

Course Format

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

Customization Options

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

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

This guided, live training—available online or on-site—is designed for quality professionals at beginner to intermediate levels who want to implement AI tools to automate inspections and enhance product quality in production settings.

Upon completion of 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 techniques to identify defects.
• Build basic AI models for anomaly detection and yield forecasting.

Training Format

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

Customization Options

• For customized training tailored to your needs, please contact us to arrange.

AI in Supply Chain and Manufacturing Logistics refers to the use of predictive analytics, machine learning, and automation to optimise inventory management, routing, and demand forecasting.

This instructor-led, live training (available online or on-site) is designed for intermediate-level supply chain professionals who wish to apply AI-driven tools to improve logistics performance, forecast demand with greater accuracy, and automate warehouse and transport operations.

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

• Understand how AI is applied across logistics and supply chain activities.
• Use machine learning models for demand forecasting and inventory control.
• Analyse routes and optimise transport using AI-based techniques.
• Automate decision-making in warehouses and fulfilment processes.

Format of the Course

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

Course Customisation Options

• To request a customised training session for this course, please contact us to make arrangements.

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 designed for beginner-level decision-makers and technical leads who want 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.

Implementing AI Use Cases utilizes a hands-on, project-based methodology to apply machine learning, computer vision, and data analytics for resolving real-world industrial challenges through the use of actual or simulated datasets.

This instructor-led live training, available both online and onsite, targets intermediate-level cross-functional teams seeking to collaboratively develop AI solutions that align with their operational objectives and build proficiency in working with industrial data pipelines.

Upon completing 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 construct machine learning solutions.
• Managing, cleaning, and analyzing varied industrial datasets.
• Demonstrating a functional prototype of an AI-driven solution tailored to a specific use case.

Course Format

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

Customization Options

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

This instructor-led, live training in Uzbekistan (online or on-site) 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 chips designed to accelerate inference and training processes in both edge computing and datacenter environments.

This instructor-led training session, available online or on-site, is designed for intermediate-level developers interested in creating and deploying AI models using the BANGPy framework and Neuware SDK on Cambricon MLU hardware.

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

• Configure and establish the development environments for BANGPy and Neuware.
• Create and optimize Python and C++ models tailored for Cambricon MLUs.
• Deploy models onto edge and data center devices operating on the Neuware runtime.
• Enhance ML workflows by integrating MLU-specific acceleration capabilities.

Course Format

• Engaging lectures coupled with interactive discussions.
• Practical, hands-on experience using BANGPy and Neuware for development and deployment.
• Guided exercises targeting optimization, integration, and testing techniques.

Customization Options

• If you require a tailored training session aligned with your specific Cambricon device model or use case, please reach out to us to arrange details.

Digital Twins are virtual replicas of physical systems enhanced by real-time data and AI-driven intelligence.

This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to build, deploy, and optimize digital twin models using real-time data and AI-based insights.

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

• Understand the architecture and components of digital twins.
• Use simulation tools to model complex systems and environments.
• Integrate real-time data streams into virtual models.
• Apply AI techniques for predictive behavior and anomaly detection.

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.

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

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

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

• Understand the architecture and key components of industrial vision systems.
• Build AI models for visual defect detection using deep learning.
• Integrate real-time inspection pipelines with industrial cameras and devices.
• Deploy and optimise AI-powered inspection systems for production environments.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and hands-on practice.
• Practical implementation in a live lab environment.

Course Customisation Options

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

Smart Robotics is the integration of artificial intelligence into robotic systems for improved 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.