Can you handle clinical or PII data with proper compliance?

Yes — for health and life-sciences contexts where GDPR, HIPAA-equivalent rules, MDR, or IRB constraints apply. Deployments stay in the institutional or regulated environment your team already operates in. We sign DPAs, work within your access controls, and document the audit trail evaluators expect.

What if the model needs retraining as new data arrives?

We document a retraining procedure your team can execute. For most research projects, retraining happens infrequently (when the next cohort lands) and a manual procedure is the right answer. If your project genuinely needs continuous retraining, we'll tell you — but it's rare for research-stage ML.

How is this different from hiring an ML engineer?

ML engineers are recurring headcount with continuous responsibilities. We're a finite engagement: 4–8 weeks to take your validated model to production, then exit. The output is documented infrastructure your team operates without us. Right fit when you need to ship, not when you need ongoing platform engineering.

Service · MLOps for research teams

MLOps Services for Research Teams

Move your validated model from a Jupyter notebook to a documented, maintainable production deployment. No Kubeflow, no hyperscale platform stack, no consultancy retainer — just the minimum viable deployment your project actually needs.

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Where research ML projects get stuck

The model is validated. The deployment is what's missing. Almost everything written about MLOps was written for platform teams running hundreds of models at scale — not for a four-person research group with one model serving forty users.

The model lives in a Jupyter notebook on someone's laptop. Reproducing it requires that exact Python version, those exact package pins, and the implicit cleaning steps in cell 3.

The PhD student who built it is graduating in four months. Their institutional knowledge of the model is leaving with them.

A previous consultancy deployed Kubeflow + Seldon for one model. Six months later nobody on the team can update it.

The model has no human-review path. Edge cases produce confidently wrong predictions and nobody notices.

Funder asked when the model will be 'available as a service'. The team's answer is some combination of *we'll figure it out* and *we don't know what that means*.

What we deliver

A research-team MLOps deployment is much smaller than the enterprise stack — and much more maintainable. Typical deliverables:

Containerised inference service

FastAPI / Flask service in a Docker container, deployable to a single VM, your institutional cluster, or a managed container service. Documented endpoints, type-validated inputs and outputs.

Confidence routing + human-review path

Threshold-based separation of high- and low-confidence predictions, with low-confidence cases routed to human review. Audit trail of which decisions used which path.

Versioned model artefacts

Model file in object storage or a registry, versioned alongside training data provenance. Rollback path explicit. Retraining procedure documented.

Model card + runbook

Structured documentation: training data, preprocessing, evaluation metrics, intended use, known limitations, version history. Increasingly funder-required for health and high-stakes contexts.

Logging and basic monitoring

Structured logs of inputs and predictions for audit, with the option to layer drift detection later. We don't build a Grafana dashboard you'll never look at; we build the substrate that lets you build one if you need it.

How an MLOps engagement runs

Four to eight weeks for a single-model deployment. We start from your validated notebook and end with a service your team operates.

Step 01

Scope (week 1)

We review your model, training pipeline, and intended usage pattern. Define the minimum viable deployment honestly — based on real users, real traffic, real retraining cadence — not aspirational architecture.

Step 02

Build (weeks 2–6)

Containerise the inference service, wire input validation and confidence routing, version the artefact, write the model card. Each component delivered with documentation as it ships.

Step 03

Hand over (week 7–8)

Deployment to your target environment, runbook walkthrough with the team, documented retraining procedure. The team can update the model without us after handover.

What this typically costs vs alternatives

An internal MLOps platform team: €120K+/year minimum (one ML engineer + one DevOps), and that's before the hiring time. A specialist consultancy on a monthly retainer: €5–8K/month indefinitely, often with vendor lock-in. A Pragma 4–8 week engagement: one-time, scoped, your team operates the deployment after handover. No retainer, no platform lock-in. Project pricing comes from the scope review, before any commitment.

What 'doing nothing' looks like: the validated model stays in a notebook on someone's laptop. The PhD student who built it graduates. Edge cases produce confidently wrong predictions and nobody notices. When the funder asks 'when will it be available as a service?' the team's answer is a vague maybe. Six months later you're paying a consultancy €6K/month to maintain a stack nobody on your team can read. The cost of acting now: 60 minutes for a free scope review.

MLOps questions research teams ask

Almost certainly not. For a single research-stage model serving low-to-moderate traffic, a containerised FastAPI service on a managed VM or container service is the right architecture. Kubernetes adds operational complexity that exceeds what your team should absorb. We'll explain the trade-offs explicitly in the scope review.

Got a model trapped in a notebook?

Send us the model, the validation results, and a short note on intended use. We'll reply within 2 business days with a scope or clarifying questions.

Request a Scope Review

Related work

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