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.
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?
Machine learning operations: the practice of deploying, monitoring, versioning and retraining models so they stay accurate and auditable in production, rather than degrading quietly after launch.
Do you set up MLOps from scratch or improve what we have?
Both. We start with a short maturity assessment, then either build the pipeline, registry and monitoring from scratch or close the gaps in what you already run.
How do you handle model drift?
Drift detection compares live data and predictions against the model's baseline and alerts before quality reaches a customer, with a retraining cadence your team controls.
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.
Which tools do you use?
Whatever fits your stack. MLOps is a discipline, not one product, so we fit the deployment, registry, monitoring and pipelines to the environment you already run rather than impose a platform.
Who runs the models after handover?
Your team. We deploy in your cloud and hand over a runbook so operating and retraining the model is a documented routine you own.
A private, on-premise WislaSearch deployment that turns a logistics operator's scattered operational knowledge into instant, cited answers - without anything leaving their network.
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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.