"Our tailored course provided a well rounded introduction and also covered some intermediate level topics that we needed to know. Clive gave us some best practice ideas and tips to take away. Fast paced but the instructor never lost any of the delegates"
Brian Leek, Data Analyst, May 2022
By the end of the day, participants will be able to:
Session 1: AI in Operational Engineering
What Actually Works
Purpose - Cut through AI hype and ground participants in engineering-relevant applications.
Key Topics
Wrap-Up
Personal action plan: One operational use case to trial in the next 90 days Key takeaways & next steps
Session 6: Implementation, Risks & Operational Readiness
Purpose - Ensure participants leave knowing how to deploy responsibly.
Key Topics
Data readiness checklist for ops teams Operational risks:Case Discussion
Where predictive maintenance fails and why Data quality, sensor placement, and organisational readiness When simpler statistical models outperform “clever” AI(Design-level, not coding)
Session 5: Asset Monitoring, Predictive Maintenance & Optimisation
Purpose - Connect AI directly to asset life, uptime, and cost reduction.
Key Topics
Predictive vs preventive maintenance Asset health scoring and degradation modelling AI inputs:Hands-On Exercise
Operational Assistant Design:Session 4: Process Automation & Decision Support
Purpose - Use AI to remove friction from day-to-day operational work.
Key Topics
AI for operational workflow automation:Practical Exercise
AI-Augmented RCA:Session 3: Root Cause Analysis & Fault Diagnosis with AI
Purpose - Move from “what happened” to “why it happened” faster and more reliably.
Key Topics
Traditional RCA vs AI-assisted RCA Using AI to:Tools Discussed (Vendor-neutral)
AI-enabled monitoring platforms Open-source anomaly detection concepts Where spreadsheets and BI still fitPractical Exercise
Incident Signal Exercise:Session 2: Incident Detection & Early Warning Systems
Purpose - Show how AI improves early detection before failures escalate.
Key Topics
AI-based anomaly detection vs threshold alerts Using AI to correlate:Activity
Operational Pain Mapping:
Operational Engineers, Reliability Engineers, Maintenance Engineers, Process Engineers, Site Engineers, Ops Managers, and Technical Leads.
"Our tailored course provided a well rounded introduction and also covered some intermediate level topics that we needed to know. Clive gave us some best practice ideas and tips to take away. Fast paced but the instructor never lost any of the delegates"
Brian Leek, Data Analyst, May 2022
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This course cuts through AI hype to show what actually works in operational engineering. Participants learn where AI adds real value across detection, diagnosis, decision support, and asset optimisation.
Through practical examples, it covers anomaly detection, incident early warning, and AI-assisted root cause analysis. Hands-on exercises demonstrate how AI reduces alert fatigue, speeds investigations, and supports daily operations.
The course explores predictive maintenance, asset health monitoring, and optimisation of resources and spares. It also addresses risks, data readiness, and governance for responsible AI deployment.
By the end, participants leave with a clear, actionable AI use case to trial in their own operations.
1. What is the target audience for this course?
This course is designed for data analysts, data scientists, machine learning engineers, and anyone interested in leveraging Language Models (LLMs) for data analysis tasks. Whether you're a beginner or an experienced professional looking to enhance your skills, this course offers valuable insights into mastering LLMs for advanced data analysis.
2. Are there any prerequisites for enrolling in this course?
While there are no strict prerequisites, a basic understanding of machine learning concepts and familiarity with Python programming language will be beneficial. Participants with a background in data analysis or related fields will find the course content more accessible, but individuals with a keen interest in data analysis are also welcome to enroll.
3. What can I expect to learn from this course?
Throughout the course, you will gain a comprehensive understanding of Language Models and their applications in data analysis. You will learn how to train and fine-tune LLMs using popular frameworks such as TensorFlow or PyTorch. Additionally, you will explore ethical considerations and potential biases in LLM-based data analysis, ensuring responsible and reliable data interpretation.
4. Will there be practical exercises and hands-on training sessions?
Yes, the course includes practical exercises and hands-on training sessions aimed at reinforcing your understanding of LLMs and data analysis techniques. You will have the opportunity to apply theoretical concepts in real-world scenarios, allowing for a deeper immersion into the subject matter.
5. How will this course benefit my career in data analysis?
By mastering LLMs and advanced data analysis techniques, you will significantly enhance your skill set and marketability in the field of data analysis. The knowledge and expertise gained from this course will open up new opportunities for career advancement and enable you to tackle complex data analysis challenges with confidence and proficiency.
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