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

This instructor-led, live training in Botswana (online or onsite) is designed for intermediate-level telecom professionals, AI engineers, and IoT specialists who wish to investigate how 5G networks accelerate Edge AI applications.

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

• Grasp the fundamentals of 5G technology and its impact on Edge AI.
• Deploy AI models optimised for low-latency applications within 5G environments.
• Implement real-time decision-making systems using Edge AI and 5G connectivity.
• Optimise 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 Botswana (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 Botswana (online or on-site) 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 optimise 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 utilises machine learning and data analytics to anticipate equipment failures and optimise maintenance schedules. It shifts maintenance strategies from reactive to proactive, thereby enhancing 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 environments.

By the conclusion of this training, participants will be capable of:

• Comprehending the distinctions between predictive maintenance and reactive or preventive maintenance strategies.
• Collecting and structuring machine data for AI-powered analysis.
• Applying machine learning models to detect anomalies and predict failures.
• Implementing end-to-end workflows that transform sensor data into actionable insights.

Course Format

• Interactive lectures and discussions.
• Hands-on exercises and case studies.
• Live demonstrations and practical data workflows.

Course Customization Options

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

Utilising Artificial Intelligence for Process Optimisation involves applying machine learning and data analytics techniques to boost efficiency, enhance product quality, and increase 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 strategies to streamline operations, minimise downtime, and foster continuous improvement initiatives.

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

• Grasp AI concepts pertinent to optimising manufacturing processes.
• Gather and prepare production data for analytical purposes.
• Deploy machine learning models to pinpoint bottlenecks and forecast equipment failures.
• Visualise and interpret data insights to facilitate evidence-based decision-making.

Course Format

• Engaging lectures complemented by group discussions.
• Extensive exercises and practical application.
• Hands-on implementation within a live laboratory setting.

Customisation Options

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

AI-driven 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 wish to utilise AI tools to automate inspections and enhance product quality in manufacturing settings.

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

• Comprehend the application of AI in industrial quality control.
• Gather and annotate image or sensor data from production lines.
• Employ machine learning and computer vision to identify defects.
• Construct 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.

Course Customization Options

• For tailored training arrangements for this course, please contact us.

The integration of AI into Supply Chain and Manufacturing Logistics involves utilising predictive analytics, machine learning, and automation to optimise inventory management, routing efficiency, and demand forecasting.

This instructor-led live training, available both online and onsite, is designed for intermediate-level supply chain professionals seeking to leverage 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 equipped to:

• Comprehend the application of AI across various 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 fulfillment operations.

Course Format

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

Course Customization Options

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

The integration of Artificial Intelligence into Smart Factories involves utilising AI technologies to automate, monitor, and optimise 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 overview of how AI can be leveraged within smart factory environments.

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

• Grasp the fundamental principles of Artificial Intelligence and machine learning.
• Identify key AI applications within manufacturing and automation sectors.
• Examine how AI facilitates predictive maintenance, quality control, and process optimisation.
• Assess the procedures involved in initiating AI-driven projects.

Course Format

• Interactive lectures and group discussions.
• Analysis of real-world case studies and collaborative exercises.
• Strategic frameworks and implementation guidance.

Course Customisation Options

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

This session offers a hands-on, project-based methodology for applying machine learning, computer vision, and data analytics to address tangible industrial challenges using real or simulated datasets.

Delivered by an instructor via live training (either online or in-person), this course targets intermediate-level, cross-functional teams aiming to collaboratively implement AI initiatives that align with their operational objectives while gaining practical experience with industrial data pipelines.

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

• Identify and define practical AI use cases within operations, quality assurance, or maintenance functions.
• Collaborate across various roles to develop machine learning solutions.
• Manage, clean, and analyse diverse industrial datasets.
• Present a functional prototype of an AI-enabled solution derived from a selected use case.

Course Format

• Interactive lectures and discussions.
• Collaborative exercises and project work in groups.
• Practical implementation within a live laboratory environment.

Customization Options

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

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

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

• Identifying security risks and vulnerabilities inherent in Edge AI deployments.
• Implementing encryption and authentication techniques to protect data.
• Designing resilient Edge AI architectures capable of withstanding cyber threats.
• Applying secure AI model deployment strategies within edge environments.

Cambricon MLUs (Machine Learning Units) are purpose-built AI chips engineered for optimal inference and training performance in both edge computing and data centre environments.

This instructor-led, live training session (available online or at your premises) is designed for intermediate 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 equipped to:

• Configure and set up the BANGPy and Neuware development environments.
• Develop and optimise Python- and C++-based models tailored for Cambricon MLUs.
• Deploy models onto edge and data centre devices operating with the Neuware runtime.
• Integrate machine learning workflows with hardware-specific acceleration capabilities.

Course Format

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

Customisation Options

• For a training programme tailored to your specific Cambricon device model or use case, please contact us to make arrangements.

Digital Twins act as virtual copies of physical entities, augmented by live data and AI-powered intelligence.

This trainer-led, live training session (available online or on-site) is designed for intermediate professionals keen on constructing, deploying, and refining digital twin models using live data and AI insights.

Upon completing this training, participants will be capable of:

• Gaining insight into the architecture and components of digital twins.
• Employing simulation tools to model intricate systems and environments.
• Integrating live data streams into virtual models.
• Applying AI techniques for predicting behaviour and identifying anomalies.

Course Format

• Interactive lectures and discussions.
• Numerous exercises and practical sessions.
• Practical implementation within a live-lab setting.

Course Customization Options

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

Artificial intelligence is revolutionising industrial computer vision, enabling manufacturers and quality assurance teams to detect surface imperfections, verify part conformity, and automate visual inspection workflows with greater efficiency.

This instructor-led live training, available online or onsite, targets intermediate to advanced QA teams, automation engineers, and developers eager to design and implement computer vision systems for defect detection and inspection using AI methodologies.

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 leveraging deep learning.
• Integrate real-time inspection pipelines with industrial cameras and hardware.
• Deploy and optimise AI-powered inspection systems within production environments.

Course Format

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

Customisation Options

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

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

This instructor-led training (available online or onsite) is designed for 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.