MLOps handoff specialists for your dedicated team.

Where MLOps handoff fits in a long-term product team.

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 your dedicated 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 takes one definition of each feature used by both paths, which is the case for a feature store and for keeping the work with one team when companies hire AI developers.

Common MLOps handoff use cases.

• A defined path out of the notebook Packaging, serving and monitoring with a clear owner.
• 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 for retraining when accuracy drops.

Adding MLOps handoff skills to your team.

The transition is planned as explicit scope, because the gap between teams is where most models stall. What that scope covers is set out in how the monthly fee is built.

MLOps handoff specialists join your team remotely as dedicated, full-time team members, selected, employed and managed by us in Chiang Mai and Bangkok. Companies that hire offshore developers through Azendo add the skill at the next monthly cycle.