MLOps Consulting
MLOps and model operations
We set up the monitoring, retraining and evaluation that keeps a live model working as your business and data change.
The problem
Your model worked well when you shipped it. Six months later, your product mix changed, a supplier changed their lead times, or your customers started writing support tickets differently. The model was not told. It kept making predictions based on a world that no longer exists, and the quality dropped quietly — not all at once, but week by week. Nobody noticed until a manager asked why the forecast had been so wrong, or a customer complained about a recommendation that made no sense. A model that nobody watches is a liability, not an asset.
You know when your model's quality drops before your customers do.
Built for: Teams that have a working model in production and no reliable way to know when it starts failing.
What we deliver
Drift detection. We set up monitoring that watches the distribution of your model's inputs and outputs over time. When they shift beyond a set threshold — because your business changed, your data changed, or your model degraded — you get an alert, not a surprise.
Evaluation harness. A consistent test set and scoring pipeline that runs on a schedule, so you have a reliable number for model quality each week rather than a vague sense of whether it is working.
Retraining pipeline. When retraining is needed, the pipeline pulls fresh data, retrains, runs the evaluation harness, and promotes the new version only if it is genuinely better than the current one. Retraining that makes things worse is not deployed.
Versioning and rollback. Every model version is stored with its evaluation results. If a new version is worse in production, you can roll back in minutes rather than rebuilding.
More in AI & Machine Learning
AI Development
A working AI system, not a proof of concept gathering dust.
AI Consulting
You know which three things to build, and which five to skip.
Enterprise AI Development
AI that your security and compliance teams can actually sign off on.
Not sure which of these fits? See the whole ai & machine learning practice, or read what we build for your industry.
Tell us what’s slow.
Describe the job eating your team’s day. We’ll tell you straight whether an agent is the right fix — and if it isn’t, we’ll say so.
Frequently asked questions
- How do we know when our model needs retraining?
- That is exactly what drift monitoring answers. Rather than retraining on a fixed calendar schedule — which may be too often or not often enough — we trigger a retraining review when the monitoring signals that something has meaningfully changed in your data or in the model's output distribution.
- We built our model with a different vendor. Can you still help?
- Usually yes. MLOps work is about the infrastructure around the model — monitoring, evaluation, pipelines — not the model itself. As long as we can call the model and inspect its inputs and outputs, we can wrap it in the tooling it needs.
- Is this only for large-scale models?
- No. Any model in production benefits from knowing when it starts failing. A simple classifier handling a few hundred predictions a day still needs someone watching it — the monitoring just needs to be proportionate to the scale, not absent.