04 / AI Deployment · MLOps
AI deployment & production infrastructure
The part most AI vendors skip: monitoring, retraining, versioning, and a deployment path that survives the model that made it into the first demo.
Who this is for
Teams with a model already built — by us, another vendor, or in-house — that works in a notebook but has no path to production, no monitoring, and no plan for what happens when its accuracy starts to drift.
The problem
A model is not done at the point it produces a good result once. It's done when it's versioned, monitored, retrainable on a schedule, and someone gets paged when its accuracy drops. This is the single most under-built part of the AI market, and it's the half of our team that most AI vendors don't have.
What we deliver
- A deployment pipeline: versioned models, reproducible training runs, staged rollout
- Drift and accuracy monitoring with alerting thresholds set from your own data
- A retraining schedule and pipeline, automated or human-gated as fits your risk tolerance
- Documented runbooks for the failure modes we can anticipate
In practice
0 → monitored
a model already in production was given drift alerting and a retraining loop for the first time
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