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Certificate in Utilising Artificial Intelligence and Machine Learning for Asset Management

Fully funded by KWETB for those in employment (T&C’s apply)
QQI, under ESS’s partnership with Griffith College (Pending Validation)
6
4-6 Weeks: Online and In-class
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9:00- 16:30 | Online Evening: 18:30 – 21:00 

2026

May

Course 1: Online: 6th and 13th  | Onsite: 16th and 29th

Course 2: Online: 6th and 13th  | Onsite: 22nd May and 13th June

The Certificate in Utilising Artificial Intelligence and Machine Learning for Asset Management programme is designed to develop learners’ understanding of

• the role of Machine Learning (ML) and Artificial intelligence (AI) in industrial asset management
• the role of ML and AI in policy, strategy & AI governance
• lifecycle asset management with ML, and
• practical AI/ML application and deployment.

The QQI-accredited Certificate in Utilising Artificial Intelligence and Machine Learning for Asset Management is a part-time programme, typically, delivered over 4 – 6 weeks (4-days in-class, 5 on-line evening sessions).

This award of 5 ECTS credits is designed to meet the industry skills shortage of qualified/workforce in the sector of Asset Management Digitalisation in manufacturing and service industries, where business has a requirement to upskill employees, and/or are driven by efficiencies, quality assurance, compliance, industry regulations and safety.

The Certificate in Utilising Artificial Intelligence and Machine Learning for Asset Management programme, which has been developed under this collaborative agreement, is designed to equip those working in industry (manufacturing, MedTech, utilities and pharmaceutical) and services with the theoretical knowledge and practical skills in deploying integrated ML and AI within an asset management system. The programme advances the education, training, and development of learners, to facilitate their working and career progression within industry. The programme has been developed for those seeking employment or career mobility, or those employed seeking to advance and/or add skills to improve performance, and often as part of that position are required to be competent in cross-functional technical skills in industry. The programme supports the development of reliability engineering capabilities by enabling learners to use AI/ML tools to analyse asset performance, reduce downtime, and improve maintenance strategies.

 

The objectives of this proposed certificate programme are to:
  • Support learners who wish to expand their skills and qualifications, equipping them to work in the manufacturing or service industries.
  • Facilitate the upskilling of those currently employed in the sector, helping them develop technical expertise, enhance job performance, and advance their careers by acquiring an approved qualification in utilising artificial intelligence (AI) and machine learning (ML) for asset management.
  • Enable individuals seeking employment or career progression to broaden their skills and qualifications, making them industry-relevant, job-ready, and competitive in the workforce.
On successful completion of the Certificate in Utilising Artificial Intelligence and Machine Learning for Asset Management programme, learners are able to:
  1. Explain the fundamentals of asset management and describe how AI/ML technologies support fault detection, predictive maintenance, and lifecycle optimisation.
  2. Design, implement, and apply AI/ML‑driven workflows to real‑world asset scenarios.
  3. Apply machine learning techniques to monitor asset performance while recognising basic governance and ethical principles.
  4. Synthesise a comprehensive AI‑enhanced asset management plan.
  • Asset Management & the Role of AI/ML
  • Asset Management Principles & Frameworks with Intelligent Extensions
  • Asset Management Policy, Strategy & AI Governance
  • Lifecycle Asset Management with ML‑Driven Optimisation
  • Tools, Implementation & Practical AI/ML Application
  • Risk Management & Ethical Considerations in AI Enhanced DAM
  • Predictive maintenance using ML algorithms
  • Fault pattern recognition and anomaly detection
  • AI-driven asset lifecycle cost modelling
  • Implementing an Asset Management System with Integrated AI/ML

The programme comprises 5 ECTS credits. It is primarily intended as a standalone award, however progression opportunities are available as follows.

 

Higher National Certificate in Manufacturing Engineering, Griffith College Limerick

This one-year part-time HNC programme delivers knowledge and skills in a range of specialist areas of manufacturing engineering and is suited to those who would like a career in the manufacturing/biopharma and the medical devices industries or those who want to upskill in this area.

Bachelor of Engineering in Industrial and Systems Engineering, Griffith College

The QQI-accredited Bachelor of Engineering in Industrial and Systems Engineering, NFQ level 7 is a one-year part-time (post-higher certificate in engineering or cognate discipline) that provides key competencies in the areas of industrial and systems engineering and related subjects.

Certificate in Industrial Manufacturing and Maintenance Skills, Griffith College in collaboration with ESS

The QQI-accredited Certificate in Industrial Manufacturing and Maintenance Skills is a one-year part-time programme that provides key knowledge, skills, and competencies in Industrial Manufacturing and Maintenance related subjects.

NFQ Level 6 (plus) engineering programmes within third-level colleges in Ireland

This programme may also facilitate learners’ application for progression to third-level programmes in engineering, science, or cognate discipline.

Entry requirements (if under 23 years) are a minimum of grade O6 / H7 in the Leaving Certificate, or equivalent, in 5 subjects. The subjects must include mathematics and English, Irish or another language.

Mature learners, i.e., applicants over the age of 23, may also apply based on work experience and / or life experience by demonstrating that they have reached the standards of knowledge, skills, and competence. Applicants’ experiential backgrounds are assessed through the submission and review of their CV as part of the application process.

The English language entry requirements for the programme are CEF B2+ or equivalent. Candidates with English language levels below CEF B2+ must first reach this minimum standard before enrolling on the academic programme.

Typical learner profiles include:

  • Craft trade and apprenticeship graduates (completion of Phase 4 required) in disciplines such as Electrical, Mechanical/Fitting, or Instrumentation. Other disciplines may be considered based on relevant experience or qualifications.
  • Individuals who hold an NFQ Level 6 qualification (or higher) in engineering, electrical, mechanical, process, instrumentation, or a related discipline.
  • Technicians, engineers, planners, supervisors, and managers who have maintained and/or managed maintenance programmes as part of a previous, current, or prospective role.

Ideally, applicants have some prior technical experience or knowledge and/or exposure to an industrial environment.

 

Event Details

Day 1: May 06, 2026
Start time: 00:00 GMT
End time: 00:00 GMT

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