MLOps managed service for models in production
Your models keep performing after go-live. We run the retraining pipelines, drift monitoring and incident response against agreed service levels, while your team keeps ownership of what the models decide.
Production models create an operating load nobody staffed for
Deployment is the start of the workload, not the end of it. Model drift, retraining, feature versioning, cost control, incidents and audit requests accumulate on the data scientists hired to build the next model, and operational work quietly displaces the modelling roadmap.
A managed service moves that operation off your team: pipelines, monitoring, retraining and incident response run to agreed service levels, with defined escalation and a periodic service review. Your people keep the decisions that matter, which models exist, what they optimise and when they are retired. The operation gets staffed either way; the choice is whether you staff it internally.
What we run, continuously
The operational cycle behind every model in production, with a human on call.
Deploy
Versioned, reproducible deployments with rollback
Monitor
Drift, performance and cost watched against thresholds
Retrain
Retraining triggered by evidence, validated before release
Report
Service levels, incidents and model changes documented for audit
What running models actually demands
Five operational realities that surface after the first deployment.
Silent drift. Performance degrades gradually and the first signal is a business complaint.
Retraining by intuition. Models are retrained when someone remembers, not when the data says so.
Data scientists on call. The people hired to build models spend their week keeping them alive.
No reproducibility. Nobody can recreate the model that produced last quarter results.
Cost out of control. Inference and training spend grow with no owner watching them.
What the service includes
We run the operation of your models in production, and your team keeps the decisions. Not for teams without models already in production: we operate existing pipelines, we do not build the first model.
Deployment pipelines
Versioned, reproducible deployment with rollback and environment parity.
Monitoring and alerting
Drift, performance, data quality and cost monitored against agreed thresholds.
Retraining and validation
Retraining triggered by evidence and validated before promotion to production.
Service reporting
Service levels, incidents, model changes and cost reported on a fixed cadence.
Where this has already been done
Excalibur, statistical modelling running inside the operation
Oilfield processes where the cost of an error is high and decisions depended on manual analysis.
We worked on Excalibur, which applies statistical and mathematical techniques to the information needed to optimise the process.
Reduced risk of error and optimised oilfield processes.
Start with an operations review
Ninety minutes with your ML and platform leads. You leave with an inventory of models in production and the service levels each one needs.
Book the operations review