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Master'sRegulation, Safety, RAMS & Homologation Applications open

Software and Dataset Traceability Engineering (AI Act), Audits and Technical Evidence for Authorities.

Online master's programme of 1,900 hours in software and dataset traceability (AI Act), audits and technical evidence for authorities. 12 modules covering the AI Act, high-risk requirements, software and dataset traceability, MLOps, AI standards, AI auditing, technical files, regulated sectors, robustness and organisational AI governance, with an assigned technical tutor.

Duration1900 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Apply the European Artificial Intelligence Act.

02

Design software and dataset traceability.

03

Manage the model life cycle with MLOps.

04

Implement technical AI management standards.

05

Audit high-risk AI systems.

06

Prepare technical evidence for authorities.

07

Establish organisational AI governance.

Profile

Who it's for

  • AI compliance managers preparing organisations for the AI Act.
  • AI system auditors assessing data, models and documentation.
  • MLOps engineers building auditable pipelines.
  • AI governance specialists setting up policies and inventories.
  • AI safety engineers working on robustness and SOTIF.
  • Technology regulatory consultants advising AI providers and deployers.
  • Engineering or science graduates looking to move into regulated sectors such as automotive.
Standards & tools

What you'll work with

  • Regulation (EU) 2024/1689 (Artificial Intelligence Act)
  • ISO/IEC 42001 (AI management systems)
  • ISO/IEC 23894 (AI risk management)
  • ISO/IEC 22989 (AI concepts and terminology)
  • ISO/PAS 8800 (safety and AI in road vehicles)
  • NIST AI RMF 1.0 (AI risk management framework)
  • ISO/IEC 5259-2 (data quality measures for analytics and machine learning)
  • Regulation (EU) 2016/679 (GDPR) (General Data Protection Regulation)
  • MLflow
  • DVC
  • Git
  • Weights & Biases
  • Fairlearn
Syllabus

12 modules · 1900 h

  1. 1.1Structure of Regulation (EU) 2024/1689
  2. 1.2Definition of an AI system
  3. 1.3Prohibited AI practices
  4. 1.4High-risk systems
  5. 1.5Transparency obligations
  6. 1.6General-purpose AI models
  7. 1.7Application timeline
  8. 1.8Authorities and the AI Office
  9. 1.9Penalty regime
  10. 1.10Case study: risk classification of a system
  1. 2.1Risk management system
  2. 2.2Data governance under Article 10
  3. 2.3Technical documentation under Annex IV
  4. 2.4Automatic event logging
  5. 2.5Transparency and information
  6. 2.6Human oversight requirements
  7. 2.7Accuracy, robustness and cybersecurity
  8. 2.8Quality management system
  9. 2.9Conformity assessment
  10. 2.10Workshop: Chapter III requirements matrix
  1. 3.1Requirements traceability
  2. 3.2Configuration management
  3. 3.3Version control with Git
  4. 3.4Continuous integration and delivery
  5. 3.5Software bill of materials (SBOM)
  6. 3.6Artefact signing
  7. 3.7Build reproducibility
  8. 3.8Traceability in ISO 26262 and ASPICE
  9. 3.9Evidence of changes
  10. 3.10Lab: SBOM and traceability in a repository
  1. 4.1Dataset life cycle
  2. 4.2Data versioning
  3. 4.3Datasheets for datasets
  4. 4.4Provenance and lineage
  5. 4.5Labelling and its quality control
  6. 4.6Bias and representativeness
  7. 4.7Use of synthetic data
  8. 4.8Data rights and licences
  9. 4.9Training and test splits
  10. 4.10Practical exercise: dataset versioning with DVC
  1. 5.1MLOps and its practices
  2. 5.2Experiment tracking
  3. 5.3Model registry
  4. 5.4Model cards
  5. 5.5Model validation
  6. 5.6Monitoring in production
  7. 5.7Data and concept drift
  8. 5.8Controlled model retraining
  9. 5.9Model retirement
  10. 5.10Case study: an auditable MLOps pipeline
  1. 6.1ISO/IEC 42001 AI management systems
  2. 6.2ISO/IEC 23894 risk management
  3. 6.3ISO/IEC 22989 concepts
  4. 6.4ISO/IEC 5259 data quality for analytics
  5. 6.5ISO/IEC TR 24027 bias
  6. 6.6ISO/PAS 8800 AI in road vehicles
  7. 6.7CEN-CENELEC JTC 21 harmonised standards
  8. 6.8NIST AI RMF
  9. 6.9Relationship with sector standards
  10. 6.10Workshop: ISO/IEC 42001 gap analysis
  1. 7.1Types of AI audit
  2. 7.2Risk-based planning
  3. 7.3Data audit
  4. 7.4Model audit
  5. 7.5Robustness testing
  6. 7.6Bias and fairness testing
  7. 7.7Explainability for auditors
  8. 7.8Audit report
  9. 7.9Follow-up of findings
  10. 7.10Lab: audit of a classifier
  1. 8.1The Annex IV technical file
  2. 8.2EU declaration of conformity
  3. 8.3Registration in the EU database
  4. 8.4Responding to authority requests
  5. 8.5Post-market monitoring of AI
  6. 8.6Reporting of serious incidents
  7. 8.7Record keeping
  8. 8.8Confidentiality and trade secrets
  9. 8.9Document management and version control
  10. 8.10Practical exercise: technical file index
  1. 9.1AI in vehicles and Regulation (EU) 2018/858
  2. 9.2AI in aviation and the EASA roadmap
  3. 9.3AI in medical devices
  4. 9.4AI in machinery
  5. 9.5AI in critical infrastructure
  6. 9.6Interaction with sector legislation
  7. 9.7Notified bodies for AI
  8. 9.8High-risk use cases
  9. 9.9Lessons from pioneering sectors
  10. 9.10Case study: AI in a perception system
  1. 10.1Adversarial attacks on models
  2. 10.2Data poisoning
  3. 10.3Robustness to perturbations
  4. 10.4Model cybersecurity
  5. 10.5Functional safety and SOTIF
  6. 10.6Scenario-based validation
  7. 10.7Uncertainty metrics
  8. 10.8Runtime monitors
  9. 10.9Stress testing
  10. 10.10Workshop: adversarial testing of a vision model
  1. 11.1Responsible AI policies
  2. 11.2Ethics and risk committees
  3. 11.3Roles: provider, deployer, importer
  4. 11.4Management of AI suppliers
  5. 11.5AI literacy
  6. 11.6AI system inventory
  7. 11.7Compliance indicators
  8. 11.8Integration with quality and privacy
  9. 11.9Compliance roadmap
  10. 11.10Practical exercise: AI system inventory for a company
  1. 12.1Selection of an AI system
  2. 12.2Risk classification
  3. 12.3Design of software and data traceability
  4. 12.4Risk management system
  5. 12.5Audit plan
  6. 12.6Proposed technical file
  7. 12.7Post-market monitoring plan
  8. 12.8Conclusions and future work
  9. 12.9Master's Thesis report
  10. 12.10Master's Thesis: defence before a panel
Methodology, software & labs

How you'll learn

100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so the programme fits around your job.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is MLflow, DVC, Git, Weights & Biases and Fairlearn, in free, academic or evaluation versions. Testing is studied through real procedures for machine learning model audits, robustness and bias testing, and preparation of technical files. Assessment is continuous, module by module, plus the final Master's Thesis.

Standards covered: Regulation (EU) 2024/1689, ISO/IEC 42001, ISO/IEC 23894, ISO/IEC 22989, ISO/PAS 8800, NIST AI RMF 1.0, ISO/IEC 5259-2, Regulation (EU) 2016/679 (GDPR).

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of Regulation (EU) 2024/1689, ISO/IEC 42001 and ISO/IEC 23894 applied to real cases, not as a theoretical annex.
Industry software:MLflow, DVC, Git, Weights & Biases and Fairlearn, in free, academic or evaluation versions.
Master's Thesis with defence:a complete AI Act compliance project for an AI system, from risk classification and traceability to the technical file and post-market monitoring, defended before a panel.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Risk classification
Classify an AI system under the AI Act and map its Chapter III requirements.
Software and data traceability
Set up SBOM, repository traceability and dataset versioning with DVC.
Auditable MLOps pipeline
Build an MLOps pipeline that produces audit-ready evidence.
AI system audit
Audit a classifier for robustness, bias and documentation.
Master's Thesis
A complete AI Act compliance project for an AI system, from risk classification and traceability to the technical file and post-market monitoring, defended before a panel.
Career opportunities

Where this takes you

AI compliance managerAI systems auditorMLOps engineerAI governance specialistAI safety engineerTechnology regulatory consultant
Admissions & scholarships

Requirements

  • University degree in engineering, science or architecture, or equivalent demonstrable professional experience.
  • Internet connection and a computer capable of running the programme software.
  • English sufficient to read technical standards.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

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FAQ

Frequently asked questions

The programme totals 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with MLflow, DVC, Git, Weights & Biases and Fairlearn, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute diploma, awarded on passing the continuous assessment and the Master's Thesis. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.

A university degree in engineering, science or architecture, or equivalent professional experience, plus a computer able to run the programme software and English sufficient to read technical standards.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

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