Services
MLOps and production ML
The unglamorous work that makes models real. Most machine learning dies after the prototype: no deployment path, no monitoring, no answer when the data drifts. We take models into production and keep them honest, with pipelines, monitoring, retraining and runbooks your engineers own.
The process we optimise
The process we optimise: keeping models that make live decisions healthy in production
A model earns its keep only once it is making live decisions and staying reliable while it does. That is a different discipline from building the model in the first place. Data shifts, behaviour changes, and a model that was accurate at launch quietly decays unless something is watching. The process we optimise is the operational one that surrounds a production model: how it is deployed, how its health is monitored, how it is retrained when it drifts, and how it is rolled back when something goes wrong. Done well, this is the unglamorous work that turns a promising prototype into infrastructure the business can depend on.
Before and after
What changes when the system is rebuilt
Before: a model that works until it doesn't
- The model lives in a notebook on someone's laptop, with no repeatable path to production
- It drifts silently, because nothing measures whether its predictions still match reality
- Retraining happens by hand, whenever someone remembers or notices it has gone stale
- There is no rollback, so a bad deployment means the live decision stays broken until it is rebuilt
After: production ML your team operates
- The model deploys through a repeatable pipeline, the same way every time
- Monitoring tracks performance and drift, and alerts a human before the decision quality slips
- Retraining runs on a defined trigger with human sign-off, rather than from memory
- A known-good version is always one rollback away, so a bad release is a quick reversal, not an outage
How we think about it
We start from the objective, then work outwards
The same discipline runs through every engagement: understand what the process is for, then design the system to serve it and measure against it.
Start from the decision the model is meant to keep making
We begin with the objective: which live decision this model drives, what good performance means for that decision, and what the cost is when it degrades. That tells us how closely it needs watching, how fast a problem must be caught, and what a safe rollback has to protect. Everything we build afterwards is sized against that, rather than against a generic best-practice checklist.
Map how the model reaches production today, honestly
We trace the real path from notebook to live decision, including the manual copy-paste steps, the retraining that happens by hand, and the absence of any monitoring or rollback. We name where the model could drift unnoticed and what would happen if it did. This is the honest audit that shows why the prototype has never been safe to lean on.
Design the operational system around that workflow
We build deployment pipelines, monitoring, alerting, retraining and rollback around how your team actually ships and operates, using your stack. Retraining is automated but gated by human sign-off, so nothing goes live unattended. The design goal is that your own engineers can run and reason about the system, not that it depends on us to keep breathing.
Measure against the objective and the decision it protects
We test the finished system against the objective from step one. Does monitoring catch a real drift before the decision degrades? Does retraining restore performance on the metric that matters? Does a rollback return to a known-good state quickly? We measure against whether the live decision stays healthy, because that, not pipeline elegance, is the point of the work.
An engagement, step by step
The following is a representative eight to sixteen week arc, not an account of a specific client. It shows the shape production ML work typically takes when a model already exists but has never been safe to run live.
- Weeks 1 to 4
Diagnose the model and the decision it drives
We establish which live decision the model serves, what good performance means for it, and how it currently reaches production. We map where it could drift unnoticed and where a bad release would have no way back.
- Weeks 4 to 8
Build the deployment pipeline
We move the model out of the notebook and into a repeatable deployment pipeline, so it ships the same way every time. This is the point at which going live stops being a manual, error-prone event.
- Weeks 8 to 12
Add monitoring, drift detection and rollback
We instrument the model so performance and drift are measured continuously and a human is alerted before decision quality slips. We put a rollback in place so a known-good version is always one step away.
- Weeks 12 to 16
Automate retraining and hand over
We wire up retraining on a defined trigger with human sign-off, then hand the estate to your team with runbooks. Your engineers operate the monitored, retrainable model themselves, rather than depending on us to keep it alive.
The engagement ends with monitored, retrainable production ML that the client's own team operates, judged on whether the live decision the model drives stays healthy without heroics.
What you get
- Deployment pipelines (CI/CD) for your models
- Monitoring and alerting on performance and drift
- Automated retraining with human sign-off
- Runbooks and handover so your team owns the estate
How it typically runs
Typical engagement: 8 to 16 weeks, 2 engineers, rolling retainer after go-live.
Diagnose
Weeks 1 to 4
Prototype pipeline
Weeks 4 to 8
Build and harden
Weeks 8 to 12
Productionise and monitor
Weeks 12 to 16
Indicative timeline. Every project is scoped individually: book a discovery call and we will provide a detailed proposal within 48 hours.
Where it fits
This practice is delivered through one of the two QuantSpark engines.
Model-neutral by default
We evaluate Claude, GPT and open-weights models head to head for every problem, and recommend whichever wins on cost, latency and accuracy.
Proof
MLOps and production ML in the field
DataControl Platform: intelligent private equity data management
A mid-market private equity firm
Predictive maintenance and ops dashboards for a UK industrial manufacturer
UK industrial manufacturer (£800m turnover)
Predicting renewals and reducing churn at scale
Enterprise cyber security software provider