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.
Software and Dataset Traceability Engineering (AI Act), Audits and Technical Evidence for Authorities. — studying from South Africa
Regulation, safety and homologation skills are essential across South African industry, from road vehicles and aviation to energy and mining. Engineers must demonstrate compliance with national standards set by the South African Bureau of Standards and sector regulators. Reliability, availability, maintainability and safety analysis is applied in infrastructure, rail, energy and manufacturing projects. Specialists support certification, type approval and risk assessment for both local and export markets.
Fees for South Africa: R 166 000 (South African rand), with instalments and employer sponsorship available.
Relevant bodies in South Africa
Typical roles
Questions from South Africa
Will a SEIUM qualification be recognised by the professional bodies in South Africa for this area?
SEIUM qualifications are private, not official state degrees, and are not accredited by any South African body. Homologation and standards work in South Africa involves the SABS and the National Regulator for Compulsory Specifications (NRCS), while ECSA handles engineering registration. Whether a SEIUM qualification is recognised is for employers or these bodies to decide, so check beforehand.
How might this qualification be used by employers in South Africa in regulation, safety and RAMS roles?
Employers in automotive, rail, energy and industrial sectors value RAMS and compliance expertise, often tied to recognised certifications and experience. A private qualification may strengthen your profile but will not replace formal requirements. Its value depends on each employer's preference, so ask hiring managers how they would assess it.
Can I study this programme alongside full-time work from South Africa, and is there a time zone or language issue?
Yes, the programme is delivered fully online in English, so you can study from South Africa without a student visa. Master's programmes require 1,900 hours, postgraduate diplomas 800 hours and specialist courses 300 hours, with an assigned technical tutor. Fees can be paid in instalments and employer sponsorship is possible. Confirm time-zone and weekly workload expectations with SEIUM before enrolling.
Take the controls.
Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Apply the European Artificial Intelligence Act.
Design software and dataset traceability.
Manage the model life cycle with MLOps.
Implement technical AI management standards.
Audit high-risk AI systems.
Prepare technical evidence for authorities.
Establish organisational AI governance.
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.
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
12 modules · 1900 h
1.1Structure of Regulation (EU) 2024/16891.2Definition of an AI system1.3Prohibited AI practices1.4High-risk systems1.5Transparency obligations1.6General-purpose AI models1.7Application timeline1.8Authorities and the AI Office1.9Penalty regime1.10Case study: risk classification of a system
2.1Risk management system2.2Data governance under Article 102.3Technical documentation under Annex IV2.4Automatic event logging2.5Transparency and information2.6Human oversight requirements2.7Accuracy, robustness and cybersecurity2.8Quality management system2.9Conformity assessment2.10Workshop: Chapter III requirements matrix
3.1Requirements traceability3.2Configuration management3.3Version control with Git3.4Continuous integration and delivery3.5Software bill of materials (SBOM)3.6Artefact signing3.7Build reproducibility3.8Traceability in ISO 26262 and ASPICE3.9Evidence of changes3.10Lab: SBOM and traceability in a repository
4.1Dataset life cycle4.2Data versioning4.3Datasheets for datasets4.4Provenance and lineage4.5Labelling and its quality control4.6Bias and representativeness4.7Use of synthetic data4.8Data rights and licences4.9Training and test splits4.10Practical exercise: dataset versioning with DVC
5.1MLOps and its practices5.2Experiment tracking5.3Model registry5.4Model cards5.5Model validation5.6Monitoring in production5.7Data and concept drift5.8Controlled model retraining5.9Model retirement5.10Case study: an auditable MLOps pipeline
6.1ISO/IEC 42001 AI management systems6.2ISO/IEC 23894 risk management6.3ISO/IEC 22989 concepts6.4ISO/IEC 5259 data quality for analytics6.5ISO/IEC TR 24027 bias6.6ISO/PAS 8800 AI in road vehicles6.7CEN-CENELEC JTC 21 harmonised standards6.8NIST AI RMF6.9Relationship with sector standards6.10Workshop: ISO/IEC 42001 gap analysis
7.1Types of AI audit7.2Risk-based planning7.3Data audit7.4Model audit7.5Robustness testing7.6Bias and fairness testing7.7Explainability for auditors7.8Audit report7.9Follow-up of findings7.10Lab: audit of a classifier
8.1The Annex IV technical file8.2EU declaration of conformity8.3Registration in the EU database8.4Responding to authority requests8.5Post-market monitoring of AI8.6Reporting of serious incidents8.7Record keeping8.8Confidentiality and trade secrets8.9Document management and version control8.10Practical exercise: technical file index
9.1AI in vehicles and Regulation (EU) 2018/8589.2AI in aviation and the EASA roadmap9.3AI in medical devices9.4AI in machinery9.5AI in critical infrastructure9.6Interaction with sector legislation9.7Notified bodies for AI9.8High-risk use cases9.9Lessons from pioneering sectors9.10Case study: AI in a perception system
10.1Adversarial attacks on models10.2Data poisoning10.3Robustness to perturbations10.4Model cybersecurity10.5Functional safety and SOTIF10.6Scenario-based validation10.7Uncertainty metrics10.8Runtime monitors10.9Stress testing10.10Workshop: adversarial testing of a vision model
11.1Responsible AI policies11.2Ethics and risk committees11.3Roles: provider, deployer, importer11.4Management of AI suppliers11.5AI literacy11.6AI system inventory11.7Compliance indicators11.8Integration with quality and privacy11.9Compliance roadmap11.10Practical exercise: AI system inventory for a company
12.1Selection of an AI system12.2Risk classification12.3Design of software and data traceability12.4Risk management system12.5Audit plan12.6Proposed technical file12.7Post-market monitoring plan12.8Conclusions and future work12.9Master's Thesis report12.10Master's Thesis: defence before a panel
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).
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Where this takes you
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.
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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