MLOps course applied to electric fleets
Online course of 300 hours in MLOps for electric fleets, structured in 6 modules covering electric fleet data, the model life cycle, deployment and operation, fleet use cases, and governance, security and regulation, with an assigned technical tutor.
MLOps course applied to electric fleets — studying from Ireland
Ireland's electromobility and energy storage sector is growing, supported by national climate and transport policy, public charging infrastructure programmes and renewable electricity generation. Research institutions and companies in Ireland work on battery systems, power electronics, hydrogen technologies and grid integration. Engineers in this area contribute to charging network design, battery management, energy storage projects and the decarbonisation of transport and industry. Policy and regulation are coordinated by government departments and the national energy regulator, alongside European rules on batteries and emissions.
Fees for Ireland: €743 (Euro), with instalments and employer sponsorship available.
Relevant bodies in Ireland
Typical roles
Questions from Ireland
Which Irish bodies regulate or recognise electromobility, battery and hydrogen engineering?
Sustainable energy and related policy in Ireland involves the Sustainable Energy Authority of Ireland and the Department of the Environment, Climate and Communications. Engineers Ireland sets professional standards for engineers. SEIUM's qualifications are private and not accredited by any national body, so recognition depends on employers or the relevant body, and you should verify it before enrolling.
Do Irish employers in batteries and hydrogen value private online qualifications?
Ireland has growing interest in electrification, energy storage and hydrogen, with employers in energy, transport and manufacturing. They typically prioritise experience, technical competence and relevant degrees. A private online qualification may support an application, but how it is judged is for each employer. No career outcome can be guaranteed.
Can I combine this with a job, and does the workload suit part-time study?
The programme is fully online and taught in English, so you can study from anywhere in Ireland without travel or a student visa. Master's programmes total 1,900 hours, postgraduate diplomas 800 and specialist courses 300. You will have an assigned technical tutor, fees can be paid in instalments, and employer sponsorship may be possible if your employer supports energy-transition training.
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
Design data ingestion and storage architectures for electric fleets.
Manage the model life cycle with MLOps tools.
Deploy models to production with containers and Kubernetes.
Monitor data drift and automate retraining.
Apply machine learning models to fleet use cases.
Assess compliance with the AI Act, the GDPR and the Data Act.
Develop a complete MLOps platform for an electric fleet.
Who it's for
- MLOps engineers bringing models into fleet operations.
- Data engineers building telemetry and charging pipelines.
- Mobility data scientists who need their models running in production.
- Data platform architects.
- Electric fleet managers looking to make data-driven decisions.
- Data governance managers responsible for compliance.
- Engineering or science graduates looking to enter fleet management.
What you'll work with
- Regulation (EU) 2024/1689 (EU Artificial Intelligence Act)
- ISO/IEC 42001 (AI management systems)
- Regulation (EU) 2016/679 (General Data Protection Regulation, GDPR)
- Regulation (EU) 2023/2854 (EU Data Act)
- ISO/IEC 27001 (information security management systems)
- ISO/IEC 23053 (framework for AI systems using machine learning)
- ISO/IEC 5338 (AI system life cycle processes)
- OCPP 2.0.1 (Open Charge Point Protocol)
- MLflow
- Apache Kafka
- Kubernetes
- Apache Airflow
- DVC
6 modules
1.1Telemetry, CAN bus, chargers and ERP1.2Streaming data ingestion1.3Apache Kafka and message queues1.4Data lakes and lakehouse architecture1.5Vehicle and charging data schemas1.6The OCPP protocol as a data source1.7Data quality and cleaning1.8Data governance and catalogue1.9Privacy of driver data1.10Workshop: telemetry ingestion pipeline
2.1From notebook to production2.2Data versioning with DVC2.3Experiment tracking with MLflow2.4Feature stores2.5Reproducible training with seeds and environments2.6Model validation before deployment2.7Model registry and promotion2.8Packaging with Docker containers2.9Orchestration with Airflow and Kubeflow2.10Practical: registering an SOH model in MLflow
3.1Batch and real-time inference3.2REST and gRPC services3.3Kubernetes and autoscaling3.4Deployment at the edge and in the vehicle3.5Canary and shadow deployment strategies3.6Continuous integration and delivery for machine learning3.7Data drift monitoring3.8Alerts and automatic retraining3.9Cloud computing costs3.10Lab: deploying a model on Kubernetes
4.1Consumption and range prediction4.2Smart charging planning4.3Predictive maintenance of vehicles4.4State of health of fleet batteries4.5Anomaly detection in chargers4.6Route optimisation with energy constraints4.7Charging demand forecasting4.8Energy management in depots4.9Fleet asset valuation4.10Case study: optimal charging in a depot
5.1Obligations under the AI Act5.2AI management systems with ISO/IEC 420015.3GDPR applied to driver data5.4Data Act and access to vehicle data5.5Information security according to ISO/IEC 270015.6Model documentation and model cards5.7Audit and traceability of decisions5.8Accountability and explainability5.9MLOps maturity of the organisation5.10Case study: compliance assessment of a model
6.1Definition of the business use case6.2Data architecture of the solution6.3Reproducible training pipeline6.4Model registry and versioning6.5Deployment and inference service6.6Monitoring and retraining policy6.7Regulatory compliance assessment6.8Estimation of operating costs6.9Report and presentation6.10Final project: final review with the tutor
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 the intake, making it compatible with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is MLflow, Apache Kafka, Kubernetes, Apache Airflow and DVC, in free, academic or evaluation versions. Practical work is based on real procedures for deployment of prediction models in fleets, drift monitoring in production and telemetry and charging data pipelines. Assessment is continuous by module, plus the final integrating project.
Standards covered: Regulation (EU) 2024/1689, ISO/IEC 42001, Regulation (EU) 2016/679, Regulation (EU) 2023/2854, ISO/IEC 27001, ISO/IEC 23053, ISO/IEC 5338, OCPP 2.0.1.
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
- No prior qualification is required.
- An 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 300 hours over 3 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.
You work with MLflow, Apache Kafka, Kubernetes, Apache Airflow and DVC, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.
A SEIUM Institute certificate, awarded on passing the continuous assessment and the final project. 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 are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.
No prior qualification is required; you need an internet connection, 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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