Models in production, kept honest.
A model that works in a notebook is a demo. We build the layer that gets it deployed, served at scale, watched for drift and retrained on a schedule — so the accuracy you measured on day one is still there in month six.
The layer between a trained model and a reliable product.
Most ML projects don't fail at the model. They fail at everything around it — serving, drift, versioning, monitoring. That's the part we own.
Model deployment & serving
Containerized inference, autoscaling GPU/CPU, batch and real-time endpoints. Versioned rollouts and one-click rollback — same rigor as any other deploy.
Training & data pipelines
Reproducible training jobs, feature pipelines, dataset versioning. Turn a one-off notebook into a system you can rebuild from scratch.
Monitoring & drift detection
Accuracy, latency, input drift, prediction distribution. Alerts that page someone when a model quietly degrades — not just when the server is down.
Feature stores & registries
One source of truth for features and model versions. The same feature in training and serving — no more train/serve skew.
Experiment tracking
Every run logged: params, metrics, artifacts. Compare experiments, reproduce the winner, ship the one that actually moved the metric.
Eval & governance
Automated test sets, regression gates before deploy, audit trails for every prediction. The unsexy half that keeps a model live and compliant.
Audit, operationalize, automate.
We start with whatever you have — a model in a notebook, a fragile cron job, a manual deploy — and turn it into a system that runs itself.
Audit
Map the path from training to production. Find the manual steps, the missing monitoring, the train/serve gaps. Output: a prioritized MLOps roadmap.
Operationalize
Containerize serving, wire CI/CD for models, stand up the registry and feature store, set up monitoring and drift alerts.
Automate
Scheduled retraining, automated evals as deploy gates, self-healing endpoints. Most clients keep us on to run it for the first 6 months.
Production-grade ML tooling.
Boring, proven tools that survive contact with a real production load. We pick from this set 95% of the time.
Where this service has shipped.
Two recent engagements that leaned heavily on this practice. Read the full case studies, or browse all work.
Custom ML model: 88% accuracy detecting how someone is traveling.
Sensor-fusion deep learning across gyroscope, accelerometer, GPS, magnetometer and barometer. Trained on multi-continent data. Inference on-device.
An AI agent that researches, writes and sends — in the rep's voice.
Multi-step research → tailored draft → multi-account send → inbox auto-reply. RAG over a public + internal knowledge base. Evals running continuously.
The questions we get most.
Anything else? Email hello@ibute.tech — we reply within 24h.
What's the difference between MLOps and DevOps?
We already have a trained model — can you just deploy it?
How do you handle model drift?
Do we actually need a feature store?
Can you run this on our cloud?
What's the difference between MLOps and AI Solutions at ibute?
Shipped across 10+ sectors.
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Get in touch
Have a mlops project in mind?
Free 30-minute review. We'll tell you whether this is the right fit, what the shape of the engagement would look like, and roughly what it costs. No deck. No follow-up unless you ask.
Austin · Pakistan · Reply within 24 hours.