MLOps is the engineering practice of running machine learning models in production: deploying them reliably, versioning models and the data they were trained on, monitoring for accuracy drift, retraining on a schedule or trigger, and rolling back safely when a new version underperforms.
Models decay quietly
Software fails loudly — an error, an alert, a page. Models fail silently. Accuracy degrades as customer behaviour, product mix or seasonality shifts away from the training data, and nobody notices until a business metric moves for reasons that take weeks to diagnose.
MLOps is the discipline of making that failure visible. Held-out evaluation running continuously, alerts on degradation, a versioned history you can roll back to, and a retraining path that does not require the original engineer to still be employed.
Process
How we deliver it
Process flow for MLOps & model deployment
01
Audit current state
How models are deployed, versioned and monitored today.
02
Build the pipeline
Reproducible training, testing and deployment as code.
03
Version everything
Model artefacts, training data snapshots and configuration.
04
Monitor
Accuracy against held-out data, input drift and latency.
05
Automate retraining
Scheduled or triggered, with approval before promotion.
Deliverables
What you receive
CI/CD pipelines for model training and deployment
Model and dataset versioning with reproducible builds
Monitoring dashboards and degradation alerting
Automated retraining with a human promotion gate
Rollback procedure, tested rather than assumed
Engagement shape
Six to twelve weeks for a first platform, then a lighter retainer. Additional models onboard quickly once it exists.
Stack decisions follow the problem. This is where we usually start, not a fixed menu.
Frequently asked
Questions we get about this
We only have one model. Is this overkill?
Possibly. For a single low-stakes model, monitoring plus a documented redeployment procedure may be enough, and we will say so. The case strengthens sharply at three or more models, or where one model materially affects revenue or compliance.
How often do models need retraining?
It depends entirely on how fast your world changes. Fashion demand models drift in weeks; equipment failure models may hold for a year. Rather than guessing a cadence, we monitor accuracy and retrain when it crosses a threshold you agree in advance.
Talk it through before you commit
A discovery call is a working session on your constraint, not a sales pitch.
