MLOps & model monitoring
Your model was accurate last year. Nobody has checked since, and it hasn't told you.
Models decay quietly
Software breaks loudly. An error appears, something stops, someone notices within the hour.
Models don't. A model that's degraded from 91% to 68% returns predictions with exactly the same confidence and formatting as it always did. Nothing errors. Nothing alerts. It just becomes gradually wrong, and the business keeps acting on it, and the discovery comes months later through a symptom nobody initially connects to the model.
The causes are ordinary. Customer behaviour shifts. A supplier changes a data format. An upstream team renames a field. Your product changes and the population no longer resembles the training set. None of these are failures — they're just time passing.
This is the discipline that catches it.
What we build
- Deployment pipelines
- Getting a model from training to serving repeatably, with versioning, staged rollout, and a rollback that works under pressure.
- Monitoring and alerting
- Tracking prediction distributions, input drift, latency, and accuracy where ground truth becomes available. Alerts to people who can act.
- Drift detection
- Automated comparison of live inputs against training distribution, flagging shifts before accuracy visibly suffers.
- Retraining pipelines
- Scheduled or triggered retraining, with validation gates so a worse model can't quietly replace a better one.
- Model registry and lineage
- Which version is live, what data trained it, who approved it, and what changed. Essential in regulated settings and useful everywhere.
- Shadow and canary deployment
- Running a new model alongside the current one on real traffic before it takes over.
Where ground truth is delayed
The awkward case: you often can't measure accuracy immediately. A churn prediction takes months to be proven right, and by then you've made decisions based on it.
So monitoring watches proxies — input drift, prediction distribution shifts, changes in downstream outcomes — and treats them as early warnings. You find out something has changed before you find out it was wrong.
Model audits
If you have models in production and nobody can say how they're performing, that's the place to start. A fixed-fee audit covering current accuracy, drift, data dependencies, and operational risk, with a prioritised list of what to fix.
Before you ask.
Probably yes for a full platform, but the basics — versioning, monitoring, a documented retraining path — are proportionate at any scale. We'll scope to your situation rather than selling you infrastructure for a team you don't have.
Machine Learning Models
A model that scores 94% in a notebook and a model that earns its keep are different achievements. The distance between them is where this work lives.
Data & CloudCloud & DevOps
Deployment should be the least interesting thing that happens all week.
Data & CloudData Engineering
Nobody has ever asked us for data engineering. They ask for the dashboard, the model, or the AI feature — and then we find out why it isn't working.
Tell us what you're trying to build.
A 30-minute call, no charge and no pitch deck. Describe the problem and we'll tell you how we'd approach it, roughly what it costs, and whether we're the right team for it. If we're not, we'll say so.
30 minutes · No charge · No deck