Data Science & ML
MLOps
The pipeline discipline that keeps models correct after launch — versioning, evaluation, monitoring and a rollback path.
100% delivery success — delivered worldwide across seven regions
What you get
Where MLOps earns its place
Models that survive contact with prod.
- Versioned data, code and models
- Evaluation on every release
- Drift monitoring and rollback
Tools & platforms
What we build it with
The stack MOD actually delivers this on.
Delivery model
How the work actually runs
One delivery model, whatever the engagement. You always know what is shipping this week and what it is worth.
Discovery
We map your sources, decisions and blockers, understand the business goal, and agree what success looks like before anything is built.
Solution design
We design the architecture, data models and use cases — right-sized to your team and your cloud — and cost the work before you commit.
Development
Weekly sprints against a visible backlog: pipelines, models, dashboards and applications, all in version control and yours from day one.
Testing & validation
Every pipeline and model is tested — with frameworks like PyTest and Great Expectations — so the numbers are trustworthy before they ship.
Deployment & training
We deploy into your cloud and train the people who will run it, so the capability lands with your team, not just our code.
Support & optimisation
Observability, alerting and continuous improvement keep the system fast, accurate and cost-efficient long after go-live.
Proof
Related work
Targeting portfolio growth for financial advisers
PII-safe client segmentation with DataIKU machine-learning pipelines that identify high-potential clients for advisers, validated with PyTest and Great Expectations.
From manual checks to automated oil-field monitoring
A custom API pings IoT sensors across oil fields into MongoDB, transformed in Python and surfaced in a Dash dashboard at individual-sensor granularity for predictive maintenance.
FAQ
Questions buyers actually ask
Straight answers. If yours is not here, ask us on a call — we will tell you if we are the wrong fit.
Ask a questionFeature stores, registries, monitoring and retraining pipelines — plus evaluation on every release and a rollback path.
Yes — we validate pipelines with PyTest and Great Expectations so models are not fed silently broken data.
Next step
Ready to find out what your data is worth?
Thirty minutes with a delivery lead, not a salesperson. You leave with a point of view on your highest-value use case and what it takes to ship it.