Diploma in Platform MLOps: CI/CD of Models and Traceability
Online postgraduate diploma of 800 hours in platform MLOps: model CI/CD and traceability. 8 modules covering ISO/IEC 42001:2023, Regulation (EU) 2024/1689 (AI Act) and ISO/IEC 23894:2023, with MLflow, Kubeflow Pipelines, DVC and KServe, with an assigned technical tutor.
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
Understand the complete life cycle of ML models.
Version data, code and models reproducibly.
Build continuous integration pipelines with ML tests.
Deploy models with safe, reversible strategies.
Ensure the traceability and lineage required by regulation.
Monitor and retrain models automatically.
Design and govern an internal MLOps platform.
Who it's for
- MLOps engineers.
- ML platform engineers building shared tooling for data science teams.
- Machine learning engineers taking models to production.
- AI architects.
- DevOps engineers moving into ML workloads.
- AI governance managers who need technical traceability.
- Engineering or science graduates aiming to enter manufacturing.
What you'll work with
- ISO/IEC 42001:2023 (AI management systems)
- Regulation (EU) 2024/1689 (Artificial Intelligence Act)
- ISO/IEC 23894:2023 (AI risk management)
- ISO/IEC 5338:2023 (AI system life cycle processes)
- ISO/IEC 25059:2023 (quality model for AI systems)
- SLSA (Supply-chain Levels for Software Artifacts)
- NIST AI RMF 1.0 (AI risk management framework)
- MLflow
- Kubeflow Pipelines
- DVC
- KServe
- Argo CD
8 modules
1.1From experiment to production1.2Hidden technical debt in ML systems1.3Google's MLOps maturity levels1.4Roles: data scientist, ML engineer and platform1.5Experiment reproducibility1.6Components of an MLOps platform1.7Managed versus open platforms1.8Requirements of industrial environments1.9Common anti-patterns in ML projects1.10Workshop: MLOps maturity assessment
2.1Code versioning with Git2.2Data versioning with DVC and lakeFS2.3Experiment tracking with MLflow2.4Model registry and stages2.5Model metadata and signatures2.6Reproducible environments with containers2.7Dependency management2.8Seeds and determinism2.9Model cards2.10Practical: model registry with MLflow
3.1Unit testing of ML code3.2Data and schema tests3.3Model behaviour tests3.4Quality thresholds as gates3.5Pipelines with GitHub Actions and GitLab CI3.6Training in CI with limited resources3.7Code and model review3.8Static analysis and dependency security3.9Artefacts and image signing3.10Lab: CI pipeline with model tests
4.1Training pipelines with Kubeflow4.2Orchestration with Argo Workflows4.3Model serving with KServe and Seldon4.4Batch and online inference4.5Canary and blue-green deployments4.6Shadow testing4.7Automatic rollback on degradation4.8GitOps with Argo CD4.9GPU management and scaling4.10Lab: canary deployment with KServe
5.1Data, code and model lineage5.2Lineage events with OpenLineage5.3Prediction traceability5.4Immutable audit logs5.5Reproducing a historical prediction5.6Approval management5.7Automatic model documentation5.8Traceability required by the AI Act5.9Evidence retention5.10Case study: reconstructing a model's lineage
6.1Service and business metrics6.2Data drift and concept drift6.3Logging inputs and outputs6.4Retraining triggers6.5Scheduled continuous training6.6Champion and challenger model validation6.7Alerts and on-call6.8Feedback with delayed labels6.9Inference and training costs6.10Practical: automated retraining loop
7.1Designing an internal ML platform7.2Templates and golden paths7.3Multi-tenancy and isolation7.4Model and supply chain security7.5AI risk management according to ISO/IEC 238947.6Approval and governance policies7.7FinOps for ML7.8Platform teams and team topologies7.9Measuring productivity7.10Workshop: designing an internal MLOps platform
8.1Use case and MLOps platform requirements8.2MLOps platform architecture8.3Versioning and registry8.4CI pipeline8.5Deployment pipeline8.6Lineage and auditing8.7Monitoring and retraining8.8Evaluating the solution8.9Report and presentation8.10Final project: final review with the tutor
How you'll learn
Training is 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 the programme fits around your job.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is MLflow, Kubeflow Pipelines, DVC, KServe and Argo CD, in free, academic or evaluation versions. Practical work is based on real procedures: CI/CD pipelines with automated model tests, canary deployments with automatic rollback, and reconstructing the lineage of a prediction. Assessment is continuous by module, plus the final project.
Standards covered: ISO/IEC 42001:2023, Regulation (EU) 2024/1689 (AI Act), ISO/IEC 23894:2023, ISO/IEC 5338:2023, ISO/IEC 25059:2023, SLSA and NIST AI RMF 1.0.
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
- Prior technical training or professional experience in the sector. No university degree is required.
- Internet connection and a computer capable of running the programme's 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 800 hours over 6–8 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.
You work with MLflow, Kubeflow Pipelines, DVC, KServe and Argo CD, 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 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.
Prior technical training or professional experience in the sector (no university degree required), a computer able to run the 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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