From Pilot to Scale with MLOps Discipline
Scaling ML is less about Kubernetes and more about contracts: data, interfaces, and ownership.
The handoff problem
Research artifacts win demos; production needs versioning, monitoring, and clear SLOs. Without shared definitions of input freshness and output quality, every release feels bespoke.
Operational habits
We help organizations adopt canary scoring, shadow deployments, and champion-challenger routines that match how your risk function already thinks about software—not as a big bang, but as measurable increments.
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