Seldon specialists for your dedicated team.

Where Seldon fits in a long-term product team.

Inference graphs are Seldon's distinguishing capability. Real systems are rarely one model: a preprocessing step, an ensemble, a router choosing a model by segment, and a combiner merging outputs. Expressing that as a deployed graph makes it visible and reviewable.

Explainability and outlier detection as first-class components matter in regulated contexts. A prediction served with an explanation, and inputs flagged as outside the training distribution, are requirements in several sectors.

What your dedicated team does with Seldon.

Graph complexity carries operational cost. Each component is a service to deploy, monitor and version, and a graph with six components has six things that can fail and a combinatorial set of version interactions.

Keeping graphs as simple as the problem permits is a design discipline held by the assigned specialists, as described in how an assignment runs.

Common Seldon use cases.

• Multi-model systems deployed transparently Routing and ensembling expressed as a visible graph.
• Explanations served with predictions Explainability as a component, where regulation requires it.
• Outliers flagged at inference Inputs outside the training distribution detected and flagged.

Adding Seldon skills to your team.

Serving architecture is assigned inside AI capacity, with graph complexity kept proportionate to the problem.

At Azendo, Seldon work sits with a dedicated MLOps team. You can add Seldon capacity as one dedicated specialist or as part of a wider remote team, managed by a service delivery manager from our offices in Thailand. Companies hire dedicated developers this way under one agreement and one monthly fee.