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

This instructor-led, live training in South Africa (online or onsite) is designed for intermediate-level telecom professionals, AI engineers, and IoT specialists keen on exploring how 5G networks accelerate Edge AI applications.

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

• Grasp the fundamentals of 5G technology and its influence on Edge AI.
• Deploy AI models optimized for low-latency applications within 5G environments.
• Implement real-time decision-making systems leveraging Edge AI and 5G connectivity.
• Optimize AI workloads to ensure efficient performance on edge devices.

The '6G and the Intelligent Edge' course offers a forward-looking exploration into the convergence of 6G wireless technologies, edge computing, IoT ecosystems, and AI-driven data processing. This integration is designed to support intelligent, low-latency, and adaptive infrastructures.

This instructor-led, live training is available both online and onsite. It is tailored for intermediate-level IT architects seeking to comprehend and design next-generation distributed architectures that leverage the synergy between 6G connectivity and intelligent edge systems.

Upon completing this course, participants will be equipped to:

• Comprehend how 6G will transform edge computing and IoT architectures.
• Design distributed systems tailored 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 edge infrastructures ready for 6G.
• 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 customized training for this course, please contact us to make arrangements.

This instructor-led, live training in South Africa (online or onsite) is aimed at advanced-level AI practitioners, researchers, and developers who wish to master the latest advancements in Edge AI, optimise their AI models for edge deployment, and explore specialised applications across various industries.

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

• Explore advanced techniques in Edge AI model development and optimisation.
• Implement cutting-edge strategies for deploying AI models on edge devices.
• Utilise specialised tools and frameworks for advanced Edge AI applications.
• Optimise 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 South Africa (online or onsite) is designed for intermediate-level developers, data scientists, and technology enthusiasts who wish to acquire practical skills in deploying AI models on edge devices for a variety of applications.

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

• Grasp the principles of Edge AI and its associated benefits.
• Set up and configure an edge computing environment.
• Develop, train, and optimise AI models for edge deployment.
• Implement practical AI solutions on edge devices.
• Evaluate and enhance the performance of models deployed on the edge.
• Address ethical and security considerations in Edge AI applications.

AI-driven predictive maintenance utilises machine learning and data analytics to anticipate equipment failures and refine maintenance schedules. This approach shifts maintenance strategies from reactive to proactive, thereby enhancing operational uptime, reducing costs, and extending asset lifespan.

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

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

• Grasp the distinctions between predictive maintenance and reactive or preventive strategies.
• Gather and structure machine data for AI-driven analysis.
• Apply machine learning models to identify anomalies and forecast failures.
• Execute end-to-end workflows transforming sensor data into actionable insights.

Course Format

• Interactive lectures and discussions.
• Practical exercises and case studies.
• Live demonstrations and hands-on data workflows.

Customisation Options

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

AI for Process Optimisation involves applying machine learning and data analytics to improve efficiency, quality, and throughput in manufacturing environments.

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

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

• Grasp AI concepts relevant to manufacturing optimisation.
• Collect and prepare production data for analysis.
• Apply machine learning models to identify bottlenecks and predict failures.
• Visualise and interpret results to support data-driven decisions.

Course Format

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

Customisation Options

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

Artificial intelligence for quality control leverages computer vision and machine learning methodologies to detect defects, anomalies, and deviations within production processes.

This instructor-led training, available either online or on-site, is designed for quality professionals at beginner to intermediate levels who aim to utilise 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 annotating image or sensor data from production lines.
• Employing machine learning and computer vision to identify defects.
• Developing basic AI models for anomaly detection and yield forecasting.

Course Format

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

Course Customisation Options

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

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

This instructor-led live training (available online or onsite) is designed for intermediate-level supply chain professionals seeking to apply AI-driven tools to enhance logistics performance, achieve accurate demand forecasts, and automate warehouse and transport operations.

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

• Grasp how AI is utilised across logistics and supply chain activities.
• Utilise machine learning models for demand forecasting and inventory control.
• Analyse routes and optimise transport using AI-based techniques.
• Automate decision-making processes within warehouses and fulfilment operations.

Course Format

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

Course Customisation Options

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

AI in Smart Factories involves applying artificial intelligence to automate, monitor, and optimise industrial operations in real time.

This instructor-led live training (available online or onsite) is designed for beginner-level decision-makers and technical leads who wish to gain a strategic and practical introduction to leveraging AI 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 optimisation.
• 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.

The implementation of AI Use Cases employs a project-focused methodology to apply machine learning, computer vision, and data analytics to address real-world industrial challenges using live or simulated datasets.

This instructor-led, live training (available online or on-site) is designed for intermediate-level cross-functional teams aiming to collaboratively implement AI solutions aligned with their operational objectives and gain hands-on 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 assurance, or maintenance.
• Collaborating across various roles to develop machine learning solutions.
• Managing, cleaning, and analysing diverse industrial datasets.
• Demonstrating a functional prototype of an AI-enabled solution based on a chosen use case.

Course Format

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

Course Customisation Options

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

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

Upon completing this training, participants will be able to:

• Identify security risks and vulnerabilities within Edge AI deployments.
• Implement encryption and authentication techniques to protect data.
• Design resilient Edge AI architectures capable of withstanding cyber threats.
• Apply secure strategies for deploying AI models in edge environments.

Cambricon MLUs (Machine Learning Units) are purpose-built AI processors designed to enhance inference and training capabilities in both edge computing and data centre environments.

This instructor-led live training, available either online or onsite, is tailored for intermediate developers looking to construct and deploy AI models leveraging the BANGPy framework and Neuware SDK on Cambricon MLU hardware.

Upon completing this training, participants will be equipped to:

• Configure and set up development environments for BANGPy and Neuware.
• Develop and refine Python and C++ models optimised for Cambricon MLUs.
• Deploy models onto edge and data centre devices running the Neuware runtime.
• Integrate machine learning workflows with MLU-specific acceleration features.

Course Format

• Interactive lectures and discussions.
• Practical application of BANGPy and Neuware for development and deployment.
• Guided exercises centred on optimization, integration, and testing.

Customisation Options

• To arrange bespoke training for this course, tailored to your specific Cambricon device model or use case, please contact us directly.

Digital twins are virtual replicas of physical systems, augmented by real-time data and AI-driven intelligence.

This instructor-led, live training (available online or on-site) targets intermediate-level professionals keen on building, deploying, and optimising digital twin models using real-time data and AI insights.

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

• Grasping the architecture and components of digital twins.
• Utilising simulation tools to model complex systems and environments.
• Integrating real-time data streams into virtual models.
• Applying AI techniques for predictive behaviour and anomaly detection.

Course Format

• Interactive lectures and discussions.
• Ample exercises and practice sessions.
• Practical implementation in a live-lab environment.

Course Customisation Options

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

The integration of AI into industrial computer vision is reshaping the way manufacturers and quality assurance teams identify surface flaws, verify part specifications, and streamline visual inspection workflows.

This live training, delivered by an instructor either online or at your site, is designed for QA teams, automation engineers, and developers at an intermediate to advanced proficiency level who aim to design and deploy computer vision systems for defect detection and inspection leveraging AI methodologies.

Upon completing this training, participants will be capable of:

• Comprehending the architecture and constituent parts of industrial vision systems.
• Developing AI models for visual defect detection using deep learning techniques.
• Integrating real-time inspection pipelines with industrial cameras and peripheral devices.
• Deploying and optimising AI-driven inspection systems within production environments.

Course Format

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

Course Customisation Options

• To request bespoke 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 online or onsite) targets advanced-level robotics engineers, systems integrators, and automation leads who aim to implement AI-driven perception, planning, and control within smart manufacturing environments.

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.

Course Format

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

Course Customization Options

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