MLOps development
The operations layer that keeps machine learning useful in production – deployment, monitoring, drift detection, retraining and a model registry, built for a regulated environment.
Results from work we have shipped
An AI-driven solution that optimises medical sales representative routes and improves field efficiency, using deep learning over visit patterns to predict the best routes.
Shortlisting a software development partner?
The questions to ask in an RFP, three warning signs a vendor may not deliver, and a scoring matrix to compare contractors objectively.
What MLOps covers?
Getting a model out of a notebook and into production behind a stable, versioned interface your systems can rely on.
Tracking accuracy, latency and data quality against the model's own targets, with alerts before a problem reaches a customer.
Catching the quiet failure – when the data shifts and a once-accurate model is wrong without anyone noticing.
Automated, reproducible retraining on a cadence your team controls, with validation before anything ships.
Every model, dataset and metric versioned, so a decision made months ago can be reproduced and explained.
The data plumbing that feeds training and inference the same features, reliably, so results do not drift between the two.
Tests, gates and approvals so a model change ships with the same discipline as a code change.
How we deliver MLOps?
The same delivery discipline on every engagement - from the first map to a handover your team runs.
We look at how your models are built and run today and find the gaps that carry the most risk.
We build the training, deployment and registry pipeline so every run is reproducible and auditable.
We add drift detection, monitoring and a retraining cadence your team can run without us.
Deployed in your cloud with a runbook, so operating the model is a documented routine, not tribal knowledge.
What shapes the work
A model that is accurate at launch is often wrong six months later, and nothing alerts you. The world moves, the data shifts, and the predictions drift out of true while every dashboard still shows green. In a scoring or risk model, that quiet drift is a real cost.
So MLOps treats monitoring, drift detection and retraining as part of the deliverable, not an afterthought. The modelling side of this lives on ML development; this page is about keeping what you built alive in production.
In a regulated setting, a prediction you cannot reproduce is a prediction you cannot defend. We version every model, dataset and metric, log each prediction with the version that made it, and keep the training runs reproducible, so a decision made months ago can be reconstructed exactly. Governance is built into the pipeline, not added when an auditor asks.
There is no one product that is MLOps. It is the deployment, the registry, the monitoring, the retraining and the pipelines working together, and the right shape depends on the stack you already run. So we fit MLOps to your environment rather than impose a platform, and hand it over as something your team operates, not a black box only we understand.
MLOps and modelling are one job in a regulated setting: how a model is validated, logged and monitored is decided while it is being built, not bolted on later. For the model building itself, see ML development; where a model needs live data to score against, the work sits next to our API integration services.
We start with a short assessment of how your models run today, which is fixed-price and tells you where the real risk is before you fund a larger build. From there the pipeline, monitoring and retraining work follows as time and material or an outcome-based arrangement, always handed over so your team runs it. The full commercial approach is on the Data Science &
What is MLOps, and why does it matter in a regulated setting?
Machine learning operations: deploying, monitoring, versioning and retraining models so they stay accurate and auditable in production instead of degrading quietly after launch. In a bank it is also the evidence layer, because reconstructing why a model decided something months ago depends on the data, the model and the code having been versioned together at the time. Building that after the fact is close to impossible, which is why we treat it as part of the model rather than a follow-on project.
Do you set up MLOps from scratch, or improve what we already run?
Both, starting from a short maturity assessment that names your specific gaps instead of scoring you against a generic model. From there we either build the pipeline, registry and monitoring, or close the gaps in what you have, inside your own cloud and your own tooling. The assessment is deliberately small, so it is worth having whether or not the rest of the work goes ahead.
How do you handle model drift?
Drift detection compares live inputs and predictions against the model's baseline and alerts before the quality reaches a customer, with thresholds set against your tolerance rather than a vendor default. The retraining cadence stays under your team's control, with validation and an approval gate before anything replaces a live model. What a regulated operation gets from this is a dated record of when behaviour changed and what was done about it.
What about model governance and audit?
Every model, dataset and metric is versioned, each prediction is logged with the version that made it, and training runs stay reproducible, so a past decision can be reconstructed and defended. That lines up with what a European supervisor already expects of the surrounding estate: DORA, Regulation (EU) 2022/2554, has applied since January 2025, and Article 8 requires a financial entity to identify and document its ICT-supported business functions and the dependencies between them. A model sitting in a decision path is one of those dependencies, and where it is a hosted third-party service it is also an entry in the Article 28(3) register of information.
How do we tell whether a partner is ready for MLOps at production scale?
Ask what they would put in place in the first month and listen for the unglamorous parts: a model registry, versioned datasets, reproducible training runs, a promotion gate, drift alerting and a rollback path. Ask whose cloud it runs in, who holds the credentials, and what your team would have to do to retrain without them. A partner who answers with their own platform product is selling you a second dependency, and under DORA a hosted platform in a decision path is a third-party arrangement you will have to register and be able to exit.
Which tools do you use?
Whichever ones fit the environment you already run. MLOps is a discipline rather than a single product, so we build the deployment, registry, monitoring and pipelines into your existing stack instead of introducing a platform your team has to adopt and your compliance function has to assess. That keeps the exit cost near zero, which is the point of doing it this way.
Who runs the models after handover?
Your team. We deploy into your cloud and hand over a runbook, so operating, monitoring and retraining a model is a documented routine you own. If we stopped work the week after handover, the models would keep running and your engineers would know how to change them.
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Models in production, quietly drifting?
Bring the models you already run. We will assess how they are deployed and monitored, and scope the MLOps to keep them accurate and auditable.