04 / AI Deployment · MLOps
Getting AI live, and keeping it working
The part most AI vendors skip, sometimes called MLOps: getting a model into real use, watching it, retraining it, and building a path that survives past the first demo.
Who this is for
Teams with a model already built, by us, another vendor, or in-house, that works well in testing but has no path to real use, no one watching it, and no plan for when its accuracy starts slipping.
What usually goes wrong
AI isn't finished the moment it works once. It's finished when someone gets alerted the moment it starts getting things wrong, and there's a tested way to retrain and fix it. This is the single most under-built part of the AI market, and it's half of what our team does.
What you get
- A repeatable way to update and roll out new versions of the model safely
- Monitoring that alerts you, using thresholds set from your own data, the moment accuracy starts slipping
- A retraining plan, automatic, or checked by a person first, depending on how much risk you're comfortable with
- Written step-by-step guides for the failure scenarios we can anticipate
In practice
0 → 100%
of past decisions reproducible against the ruleset in force at the time, UtterVision
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