Diploma in Production Deployment of Solvers and Optimization APIs
Online postgraduate diploma of 800 hours in putting solvers and optimisation APIs into production. 8 modules covering optimisation-as-a-service architecture, model engineering, asynchronous API design, containerised deployment and scaling, regression testing, planner interaction and service governance under OpenAPI 3.1, JSON Schema 2020-12 and ISO/IEC 25010:2023, 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
Design the architecture of a production optimisation service.
Apply software engineering best practice to mathematical models.
Build asynchronous APIs for problem solving.
Deploy and scale solvers in containers and in the cloud.
Ensure quality and performance with regression testing.
Support interaction with planners and build their trust.
Operate and govern the service throughout its life cycle.
Who it's for
- Optimisation software engineers.
- Operations research engineers who need their models to run reliably in production.
- Backend engineers.
- Planning solutions architects.
- DevOps engineers.
- Decision scientists.
- Engineering or science graduates seeking to enter logistics.
What you'll work with
- OpenAPI Specification 3.1 (REST API description standard)
- JSON Schema 2020-12 (Data validation schemas)
- ISO/IEC 25010:2023 (Software product quality model)
- ISO/IEC 27001:2022 (Information security)
- OpenTelemetry Specification (CNCF observability standard)
- MPS format (Mathematical Programming System)
- Gurobi
- Pyomo
- FastAPI
- Celery with Redis
- Kubernetes
8 modules
1.1From the notebook model to the production system1.2Usage patterns: batch and on-demand planning1.3Latency and solve-time requirements1.4Architecture of an optimisation service1.5Solver licensing in production1.6Separation of model, data and solver1.7Input and output contract1.8Teams and responsibilities1.9Anti-patterns in optimisation services1.10Workshop: architecture of a planning service
2.1Code structure of a model2.2Algebraic layers: Pyomo, JuMP and linopy2.3Input data validation2.4Infeasibility detection and IIS2.5Diagnosing unbounded models2.6Unit testing of constraints2.7Reproducibility and seeds2.8Parameter configuration2.9Mathematical documentation of the model2.10Practical exercise: test suite for a Pyomo model
3.1REST design with OpenAPI3.2Request schemas with JSON Schema3.3Asynchronous execution and jobs3.4Task queues with Celery and Redis3.5Job states and cancellation3.6Partial solutions and time limits3.7Callbacks and webhooks3.8API versioning3.9Authentication and authorisation3.10Lab: asynchronous API with FastAPI and Celery
4.1Containers with solvers4.2Licence management in containers4.3Kubernetes and horizontal scaling4.4CPU and memory allocation4.5Cloud solvers: Gurobi Cloud and others4.6Optimisation in serverless environments4.7Concurrency and priority queues4.8Cost per solve4.9High availability of the service4.10Practical exercise: Kubernetes deployment with autoscaling
5.1Benchmarking on real instances5.2Solution regression testing5.3Variability of solver performance5.4Time limit and acceptable quality5.5Warm starts with previous solutions5.6Automatic parameter tuning5.7Observability: logs, metrics and traces5.8Monitoring the gap and feasibility5.9Degradation alerts5.10Lab: solution regression test bench
6.1Explainability of solutions6.2What-if analysis and scenarios6.3Locks and manual adjustments6.4Soft constraints and prioritisation6.5Visualisation of plans and Gantt charts6.6Comparison of plans6.7Planner trust6.8Handling business exceptions6.9Feedback to improve the model6.10Case study: plan review interface
7.1CI/CD for optimisation models7.2Model change management7.3Security of planning data7.4Integration of the service with ERP and MES7.5Service level agreements7.6Second-level support7.7Measuring the value generated7.8Operational documentation and manuals7.9Evolution roadmap7.10Workshop: service operation runbook
8.1Problem and users8.2Optimisation model8.3API and data contract8.4Deployment infrastructure8.5Testing and benchmarking8.6Observability of the optimisation service8.7User interface8.8Operation plan for the optimisation service8.9Report and presentation8.10Final 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 each intake, so the programme is compatible with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Gurobi, Pyomo, FastAPI, Celery with Redis and Kubernetes, in free, academic or evaluation versions. Practical work is based on real procedures for solution regression testing with real instances, load testing of optimisation APIs and what-if analysis with planners. Assessment is continuous by module plus the final project.
Standards covered: OpenAPI Specification 3.1, JSON Schema 2020-12, ISO/IEC 25010:2023, ISO/IEC 27001:2022, OpenTelemetry Specification, MPS format.
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 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 Gurobi, Pyomo, FastAPI, Celery with Redis and Kubernetes, 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 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.
Related programmes
Diploma in Demand Models and Control Strategies
Online postgraduate diploma of 800 hours in demand models and control strategies: 8 modules covering mobility demand, discrete choice models, activity-based models, short-term demand forecasting, traffic…
Diploma in Time Series Models for Engineering (ARIMA→DL)
Online postgraduate diploma of 800 hours in time series models for engineering, from ARIMA to deep learning. 8 modules covering ARIMA and state-space models, machine learning…
Diploma in Roadmap, Data SLAs and Service Contracts
Online postgraduate diploma of 800 hours in data roadmaps, data SLAs and service contracts, with 8 modules covering data product management, data roadmaps, data service levels,…
More in Data, AI, Simulation & Analytics
All Data, AI, Simulation & Analytics programmes → · How to apply · Fees & financing · Scholarships