The unglamorous work that keeps a model working.
A model that scored well in a notebook and a model that's still accurate six months into production are two different engineering problems. MLOps is the second one.
The infrastructure most AI pitches skip.
- Model versioning and reproducibility — so "which model is actually in production" has a real answer, not a guess.
- Monitoring for drift and degradation — catching a model that's quietly gotten worse before a customer does.
- Retraining pipelines that run on a schedule or a trigger, instead of "whenever someone remembers."
- Cloud infrastructure and DevOps sized to actual inference load — audits that have measurably cut hosting costs on past projects, not just added tooling.
Usually: a model that already shipped.
MLOps consulting makes the most sense once something's already in production and the question has shifted from "does this work" to "will this still work in a year." If you're earlier than that — still deciding whether to build — start with machine learning consulting instead. If the model exists but the product around it doesn't, that's custom AI development.
Production discipline, in a real build.
Tracking down an App Store rejection to a Core ML compute-unit fallback on older iPadOS builds — the kind of production issue that only shows up after a model ships, and the reason MLOps discipline matters before launch, not after.
Read the case study →Tell us what's already in production.
We'll audit what's there and tell you honestly what needs shoring up before it breaks.