Service · Operate
MLOps that keeps models accountable in production.
Reproducible training, automated deployment, drift detection, and the monitoring you need to know your model is still right, long after the data scientist has moved on.
What we deliver
Capabilities, not departments.
Production model deployment
Containerized inference, autoscaling, blue/green and canary rollouts.
Training pipelines
Reproducible training with lineage tracking and scheduled retrains.
Feature stores
Online and offline parity for low-latency, consistent inference.
Model monitoring
Latency, throughput, error rates, and the business KPIs that actually matter.
Drift & quality detection
Data drift, prediction drift, and alerts before customers notice.
Platform stand-up
Zero-to-production MLOps pipelines on AWS, GCP, or Azure.
How we deliver
From notebook to production rails.
Audit your stack. Stand up the pipeline. Migrate models. Operate them with confidence.
- Models in production7all on EC2 · no autoscale
- Training environmentNotebooksno lineage · no reruns
- Feature parityDriftonline ≠ offline
- MonitoringNonealerts via email
- DeploymentManualengineer SSHs in
Engagement flow
From first ping to production-grade rails.
Seven steps. Honest scope. No twelve-week discovery decks.
Two-minute intake
Six fields: stack, pain points, SLA, launch date, budget, contact. We come into the discovery call already focused.
Time
2 min
How we engage
Pick how you start.
From a one-week audit to ongoing platform operation, meet us where you are.
FAQ
Questions clients ask before we start.
Ready to ship models that stay in production?
Tell us what you're building, we'll tell you how we'd ship it.