MLOps and Model Deployment Backbone for a Regional Hospital Network's Triage AI
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Problem
Hand-deployed models, no monitoring
An 18-hospital regional network had piloted imaging triage models for stroke and other time-critical findings, but every deployment was manual: models copied onto individual servers, versions untracked and no monitoring in place. One pilot model degraded silently for weeks before anyone noticed, and each new site rollout consumed over a month of ad-hoc IT effort.
Solution
Containerised MLOps deployment backbone
We built the network's MLOps and deployment backbone: containerised model serving behind a central registry, CI/CD pipelines with automated validation gates against curated test sets, staged hospital-by-hospital rollouts, and live dashboards tracking drift, latency and alert volumes. Every prediction is logged for audit, and rolling back to any previous model version takes a single command.
Measurable Impact
What changed after launch
Model deployment lead time cut from over 6 weeks of manual work to under 3 days
Input drift on a triage model detected and rolled back within 48 hours of onset
99.6% model-serving uptime sustained across all 18 hospitals in the first year
Scan-to-care-team alert latency held under 90 seconds at the 95th percentile
Tech & Tools Used
What powered the build
Ready to Build your Hospitals & Health Systems Business with MLOps & Model Deployment
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