MLOps handoff.

Where MLOps handoff fits on a long engagement.

The gap is organisational as much as technical. Data scientists optimise for model quality and are not usually accountable for uptime; engineers own reliability and did not build the model. Without someone responsible for the transition, models accumulate in notebooks with demonstrated value and no path to production.

The work itself is considerable and consistently underestimated. Packaging with pinned dependencies, a serving interface, feature computation that matches training exactly, monitoring for both system and model behaviour, and a retraining path. A model in production is a system, not an artefact.

What an assigned team does with MLOps handoff.

Training-serving skew is the defect that quietly ruins deployed models. Features computed one way in training and another way at inference produce a model that scored well and performs poorly, with nothing in the logs to indicate why.

Preventing it means one definition of each feature used by both paths, which is the argument for a feature store and for scoping this work under ai engineering services rather than leaving it between two teams.

What we use MLOps handoff for.

• A defined path out of the notebook Packaging, serving and monitoring owned rather than left between functions.
• Training and serving features aligned One definition used by both, so the deployed model behaves as it scored.
• Retraining planned before it is needed A defined trigger and path, rather than a manual rebuild when accuracy drops.

How MLOps handoff capacity is assigned.

The transition is assigned as explicit scope rather than left between two functions, because a gap between teams is where most models stop. What that scope covers is set out in how the monthly fee is built.