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

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

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

• Grasp the fundamentals of 5G technology and its influence on Edge AI.
• Deploy AI models optimised for low-latency applications within 5G environments.
• Implement real-time decision-making systems leveraging Edge AI and 5G connectivity.
• Optimise AI workloads to ensure 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:

• Grasp 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 Australia (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 the performance and efficiency of Edge AI solutions.
• Investigate 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 Australia (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 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. This approach shifts organisations from reactive maintenance models to proactive strategies, facilitating improved operational uptime, reduced costs, and extended asset longevity.

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

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

• Differentiate predictive maintenance from reactive and preventive maintenance strategies.
• Collect and structure machine data for AI-driven analysis.
• Apply machine learning models to identify anomalies and forecast failures.
• Implement end-to-end 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.

Course Customisation Options

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

AI for Process Optimisation involves applying machine learning and data analytics to boost efficiency, product quality, and throughput within manufacturing environments.

This instructor-led live training (available online or onsite) is designed for intermediate-level manufacturing professionals looking to utilise AI techniques to streamline operations, minimise downtime, and support continuous improvement efforts.

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

• Grasp AI concepts pertinent 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 decision-making.

Course Format

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

Course Customisation Options

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

AI for Quality Control involves leveraging computer vision and machine learning techniques to identify defects, anomalies, and deviations within production processes.

This instructor-led, live training is available both online and onsite. It is designed for quality professionals at beginner to intermediate levels who wish to apply 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.
• Utilise machine learning and computer vision to detect defects.
• Develop simple AI models for anomaly detection and yield forecasting.

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 in Supply Chain and Manufacturing Logistics involves applying predictive analytics, machine learning, and automation to optimise inventory levels, routing efficiency, and demand forecasting.

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

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

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

Format of the Course

• Interactive lecture and discussion.
• Numerous exercises and practice opportunities.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

The integration of artificial intelligence into smart factories involves applying AI technologies to automate, monitor, and optimise industrial operations in real time.

This instructor-led live training, available either online or onsite, is designed for beginner-level decision-makers and technical leads who wish to gain a strategic and practical overview of how AI can be leveraged within smart factory environments.

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

• Comprehend the core principles of AI and machine learning.
• Identify key AI use cases within manufacturing and automation sectors.
• Explore how AI supports predictive maintenance, quality control, and process optimisation.
• Evaluate the steps involved in launching AI-driven initiatives.

Course Format

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

Course Customisation Options

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

AI Use Case Implementation employs a hands-on, project-based methodology to leverage machine learning, computer vision, and data analytics for addressing real-world industrial challenges, utilising actual or simulated datasets.

This instructor-led, live training (available online or onsite) targets intermediate-level cross-functional teams seeking to collaboratively implement AI solutions aligned with their operational objectives while gaining practical experience with industrial data pipelines.

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

• Identify and scope practical AI use cases within operations, quality assurance, or maintenance.
• Collaborate across different roles to develop machine learning solutions.
• Manage, clean, and analyse diverse industrial datasets.
• Demonstrate 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.
• Hands-on implementation within a live-lab environment.

Course Customisation Options

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

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

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

• Comprehend the security risks and vulnerabilities associated with Edge AI deployments.
• Implement encryption and authentication techniques to protect data.
• Design resilient Edge AI architectures capable of withstanding cyber threats.
• Apply secure AI model deployment strategies within edge environments.

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

This instructor-led live training, available online or onsite, is designed for intermediate-level developers looking to build and deploy AI models on Cambricon MLU hardware using the BANGPy framework and Neuware SDK.

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

• Set up and configure the BANGPy and Neuware development environments.
• Develop and optimise Python- and C++-based models specifically for Cambricon MLUs.
• Deploy models to edge and data centre devices running the Neuware runtime.
• Integrate machine learning workflows with MLU-specific acceleration features.

Course Format

• Interactive lectures and discussions.
• Hands-on development and deployment using BANGPy and Neuware.
• Guided exercises focusing on optimisation, integration, and testing.

Customisation Options

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

Digital Twins serve as virtual replicas of physical systems, amplified by real-time data feeds and AI-driven intelligence.

This instructor-led, live training session (available online or onsite) is designed for intermediate-level professionals looking to build, deploy, and optimise digital twin models leveraging real-time data and AI-based insights.

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

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

Course Format

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

Customisation Options

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

The application of artificial intelligence in industrial computer vision is revolutionising the way manufacturers and quality assurance (QA) teams identify surface defects, verify part compliance, and automate visual inspection processes.

This instructor-led live training, available either online or onsite, is designed for intermediate to advanced QA 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 capable of:

• Understanding the architecture and components of industrial vision systems.
• Building AI models for visual defect detection using deep learning.
• Integrating real-time inspection pipelines with industrial cameras and devices.
• Deploying and optimising AI-powered inspection systems for production environments.

Format of the Course

• Interactive lecture and discussion.
• Numerous exercises and practice opportunities.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

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

This instructor-led live training (available online or on-site) is designed for advanced-level robotics engineers, systems integrators, and automation leads seeking 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 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.
• Numerous exercises and practical practice.
• Hands-on implementation in a live lab environment.

Course Customisation Options

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