Overview & Challenge
40 Models, Zero Shared Plumbing
Corelink's product teams had built 40 models — churn, upsell, fraud, routing — each deployed its own artisanal way: cron jobs on forgotten VMs, notebooks with hardcoded paths, nobody sure which model served which endpoint.
Releases took weeks, rollbacks were scary, and the CTO couldn't answer the auditor's simplest question: which model version made that decision?
Approach & Solution
Boring Releases For Exciting Models
We built LoopLine: a single paved road from training to serving. Every model gets versioned data, containerized serving, canary rollouts and drift alarms — the same way, every time, enforced by the pipeline rather than by policy documents.
- Model registry & lineageEvery prediction traceable to data, code and approver.
- Canary deploymentsNew versions prove themselves on 5% traffic first.
- Drift & quality alarmsAutomatic rollback triggers when behavior shifts.
40
Models on one pipeline
−83%
Time from train to production
5 min
One-click rollback anywhere
100%
Predictions with full lineage
Related Service
The same MLOps discipline ships inside every Machine Learning engagement we run.
