Why Choose this Training Course

This AI in Rail training course provides a strategic and technical deep dive into the integration of artificial intelligence within modern railway engineering. Designed to bridge the gap between data science and rail operations, the curriculum begins with the fundamentals of machine learning and data governance, specifically tailored to the complexities of railway datasets. Participants will explore how AI-driven decision support systems revolutionize traffic management, scheduling, and logistics, moving beyond theory into practical applications that enhance network efficiency and support net-zero energy goals.

A significant portion of the AI in Rail training is dedicated to the high-stakes environment of Safety and Governance. You will engage with critical regulatory frameworks, such as ISO/IEC TR 5469:2024, and master risk management tools like CyRail for cybersecurity modeling. The syllabus also prioritizes hands-on experience in Predictive Maintenance and Condition Monitoring, featuring workshops on building failure-pattern models for rolling stock and infrastructure.

As the industry evolves, the AI in Rail training addresses cutting-edge developments in Large Language Models (LLMs), demonstrating how tools like RAPORS can streamline safety case generation and compliance with standards like EN50126. The journey concludes by equipping you with the organizational strategy needed to lead AI adoption, aligning workforce skills with the AI Skills for Business Competency Framework. Through a blend of simulation-based case studies (including OLESense and derailment prevention) and strategic roadmap development, this course ensures you are ready to implement resilient, ethical, and highly efficient AI solutions in the rail sector.

We are also a proud member of the Energy Institute of UK.

Who Should Attend

• Railway Systems Engineer
• Asset Management Engineer
• Rolling Stock / Fleet Engineer
• Signal & Telecommunications (S&T) Engineer
• Rail Operations Manager
• Network & Timetable Planner
• Freight & Logistics Coordinator
• Control Center Duty Manager
• System Assurance & Safety Case Engineer
• Cybersecurity Specialist (Rail)
• Regulatory Compliance Manager
• AI Ethics & Governance Officer
• Chief Technology Officer (CTO)
• Head of Digital Transformation
• Data Scientist / Data Analyst
• AI Product Manager
• Innovation Lead (Transport)
• Maintenance Strategy Manager

Key Learning Objectives

• Grasp the core mechanics of supervised, unsupervised, and reinforcement learning tailored to the unique data quality and governance needs of the railway sector.
• Evaluate AI-driven decision support systems to enhance real-time scheduling, demand forecasting, and network efficiency for both passenger and freight operations.
• Develop anomaly detection models and condition monitoring systems to transition rolling stock and infrastructure from reactive to predictive maintenance.
• Navigate critical regulatory frameworks, including ISO/IEC TR 5469:2024 and EN50126, while modeling cybersecurity risks using tools like CyRail.
• Utilize Large Language Models (LLMs) and autonomous monitoring tools like OLESense to automate safety documentation, gap analysis, and infrastructure inspection.
• Design robust AI business cases and project roadmaps aligned with the AI Skills for Business Competency Framework to lead organizational transformation and sustainability.

Enquiry Form