Master’s in Industrial Data Engineering & Platform MLOps
Online master's programme of 1,900 hours in industrial data engineering and platform MLOps: data lakes, feature stores, cataloguing and data quality. 12 modules covering industrial data sources, OT/IT ingestion, storage and modelling, batch and streaming processing, data quality and observability, data platform DevOps, the ML model life cycle, platform MLOps at scale, industrial edge computing, security and compliance, and reference industrial cases, with an assigned technical tutor.
Master’s in Industrial Data Engineering & Platform MLOps — studying from South Africa
South Africa has a growing digital economy, with strong demand for data scientists, analysts and simulation specialists in finance, mining, telecoms and government. Major banks, mining houses and telecommunications operators such as MTN and Vodacom rely on advanced analytics for planning and operations. Universities including the University of Cape Town and Wits contribute research in machine learning and computational modelling. Protection of personal data is governed by national legislation, which shapes how analytics work is carried out.
Fees for South Africa: R 110 000 (South African rand), with instalments and employer sponsorship available.
Relevant bodies in South Africa
Typical roles
Questions from South Africa
Is a SEIUM qualification recognised by any South African professional body for data and AI?
South Africa has no single statutory register for data scientists or AI practitioners, and bodies such as the Computer Society of South Africa and the South African Qualifications Authority set their own standards. SEIUM is a private online institute whose qualifications are not state degrees and are not accredited by a national body. Recognition is decided by employers or the relevant body, so ask them directly before enrolling.
Do South African employers in data and AI value private online qualifications?
Demand for data and analytics skills in South Africa spans banking, insurance, telecoms, retail and government, and employers often assess skills through portfolios, technical interviews and experience. A private online qualification may support your CV, but it does not guarantee a job or salary outcome. Show prospective employers the syllabus and your project work to gauge their interest.
How does the workload fit with a job, and what English level do I need?
The programme is fully online and taught in English, so you need confident reading and writing skills. As it is remote, no student visa is needed to study from home. Master's programmes total 1,900 hours, postgraduate diplomas 800 hours and specialist courses 300 hours, with an assigned technical tutor and a final project. Fees can be paid in instalments, and employer sponsorship may be possible.
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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
Integrate OT and IT data sources into scalable platforms.
Design robust batch and streaming pipelines for industrial data.
Ensure the quality and traceability of plant data.
Deploy data platforms using DevOps practices and infrastructure as code.
Operate the full life cycle of machine learning models in production.
Apply industrial cybersecurity and European regulatory requirements.
Evaluate the cost and performance of industrial data architectures.
Who it's for
- Industrial data engineers.
- MLOps engineers working with plant data.
- Data platform architects.
- OT/IT integration engineers.
- Platform engineers moving into industrial environments.
- Industrial analytics managers.
- Engineering or science graduates looking to enter advanced manufacturing.
What you'll work with
- IEC 62264 (ISA-95) (enterprise-control system integration)
- IEC 62541 (OPC UA) (industrial interoperability)
- IEC 62443 (industrial automation cybersecurity)
- ISO/IEC 23053 (framework for AI systems using machine learning)
- ISO 8000 (data quality)
- Directive (EU) 2022/2555 (NIS2) (cybersecurity of network and information systems)
- Regulation (EU) 2023/2854 (Data Act) (access to and use of data)
- ISO/IEC 27001 (information security management)
- Apache Kafka
- Apache Spark
- MLflow
- Kubeflow
- Terraform
12 modules
1.1The ISA-95 pyramid and automation levels1.2Sensors, PLCs, SCADA and historians1.3OPC UA, Modbus and MQTT protocols1.4Process time series1.5Quality, MES and ERP data1.6Particularities of OT data versus IT data1.7Latency, sampling and synchronisation1.8Contextualisation with asset models1.9Quality challenges in plant data1.10Case study: data inventory of a packaging line
2.1Edge gateways and data acquisition2.2Unified Namespace architecture2.3MQTT brokers and Sparkplug B2.4Apache Kafka for industrial events2.5Change data capture from databases2.6Buffering and tolerance to disconnections2.7Network segmentation according to IEC 624432.8Compression and bandwidth2.9Schema and version management2.10Lab: OPC UA ingestion into Kafka
3.1Time-series databases3.2Lakehouse with Delta Lake and Iceberg3.3Partitioning and retention by asset3.4Modelling equipment hierarchies3.5Master data and asset coding3.6Event and alarm schemas3.7Optimising analytical queries3.8Cost of hot and cold storage3.9Backup and recovery3.10Practical exercise: data model for a multi-site plant
4.1Apache Spark for large volumes4.2Stream processing with Flink4.3Time windows and watermarks4.4Real-time calculation of production KPIs4.5Orchestration with Airflow and Dagster4.6Declarative transformations with dbt4.7Idempotency and reprocessing4.8Handling late and duplicate data4.9Pipeline testing4.10Lab: streaming OEE calculation
5.1Range validation and physical consistency5.2Detecting frozen and noisy sensors5.3Gap imputation in time series5.4Lineage with OpenLineage5.5Freshness and completeness metrics5.6Quality alerts and incident management5.7Data contracts in OT environments5.8Industrial data catalogue5.9Traceability auditing5.10Workshop: quality monitor for sensor data
6.1Infrastructure as code with Terraform6.2Containers and Kubernetes6.3GitOps and reproducible environments6.4CI/CD for data pipelines6.5Secrets and credentials management6.6On-premise, cloud and hybrid platforms6.7Autoscaling and capacity6.8FinOps applied to data platforms6.9Templates and golden paths for teams6.10Practical exercise: deploying a platform with Kubernetes
7.1From notebook to production code7.2Experiment management with MLflow7.3Feature stores for industrial data7.4Distributed, reproducible training7.5Model registry and versioning7.6Model packaging and serving7.7Batch, online and edge inference7.8Model testing and validation7.9Documentation with model cards7.10Lab: MLOps pipeline for quality prediction
8.1MLOps maturity levels8.2Kubeflow and Vertex AI platforms8.3Automated retraining pipelines8.4Canary and shadow deployments8.5Monitoring data and concept drift8.6Inference performance and latency8.7Managing multiple models per asset8.8Feedback from operators8.9Model auditing and governance8.10Case study: model fleet for a hundred machines
9.1Edge, fog and cloud architectures9.2Inference on industrial devices9.3Remote management of edge fleets9.4Secure OTA updates9.5Compute and power constraints9.6Edge-cloud synchronisation9.7Lightweight containers on the plant floor9.8Edge device security9.9Low-latency use cases9.10Lab: model deployed on an edge gateway
10.1Industrial cybersecurity with IEC 6244310.2Zones, conduits and industrial DMZ10.3Access control and the principle of least privilege10.4Encryption in transit and at rest10.5The NIS2 Directive and its obligations10.6The Data Act and access to machine data10.7The AI Act and high-risk systems10.8Vulnerability management on the platform10.9Logging and evidence for audit10.10Case study: risk analysis of an OT platform
11.1Predictive maintenance of rotating equipment11.2Advanced statistical quality control11.3Plant energy optimisation11.4Batch traceability in the food industry11.5Machine vision for inspection11.6Process digital twin11.7Data-driven production planning11.8Analytics for fleets of sold equipment11.9Lessons learnt from failed deployments11.10Workshop: architecture design for a real case
12.1Case definition and requirements12.2Analysis of sources and connectivity12.3Proposed platform architecture12.4Pipeline implementation12.5ML model and MLOps cycle12.6Observability and quality12.7Security and compliance12.8Performance and cost evaluation12.9Thesis report12.10Master's Thesis: defence before a panel
How you'll learn
The programme is delivered 100% online 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 it fits around your job.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Apache Kafka, Apache Spark, MLflow, Kubeflow and Terraform, in free, academic or evaluation versions. Exercises are based on real procedures: deploying pipelines with real plant data, load testing the streaming platform and validating models in an edge environment. Assessment is continuous by module, plus the final Master's Thesis.
Standards covered: IEC 62264 (ISA-95), IEC 62541 (OPC UA), IEC 62443, ISO/IEC 23053, ISO 8000, Directive (EU) 2022/2555 (NIS2), Regulation (EU) 2023/2854 (Data Act), ISO/IEC 27001.
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 Apache Kafka, Apache Spark, MLflow, Kubeflow and Terraform, 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), a suitable computer 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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