---
title: "Seldon | Skills We Assign For | Azendo"
description: "Seldon for model deployment — inference graphs, explainers, plus the Azendo roles assigned for it."
url: "https://azendo.co/skills/seldon/"
---

[Skills](https://azendo.co/skills/) MLOps and model delivery 

# Seldon.

Seldon is a model deployment platform for Kubernetes supporting inference graphs — chains of models, transformers, routers and combiners — as well as explainability and outlier detection components.

## Where Seldon fits on a long engagement.

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 is more honest than hiding it in application code.

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

## What an assigned 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](https://azendo.co/how-it-works/).

## What we use Seldon for.

* Multi-model systems deployed honestly Routing and ensembling expressed as a graph rather than hidden in application code.
* Explanations served with predictions Explainability as a component, where regulation requires it.
* Outliers flagged at inference Inputs outside the training distribution detected rather than scored confidently.

## How Seldon capacity is assigned.

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

## Service lines it sits in

* [MLOps engineering](https://azendo.co/services/mlops-engineering/)

Capacity is agreed as a committed monthly capacity across a discipline, not per skill.

## Related in mlops and model delivery

* [TensorRT — skill we assign for](https://azendo.co/skills/tensorrt/)
* [Model registry — skill we assign for](https://azendo.co/skills/model-registry/)
* [Weights & Biases — skill we assign for](https://azendo.co/skills/weights-and-biases/)
* [Comet — skill we assign for](https://azendo.co/skills/comet/)
* [Neptune.ai — skill we assign for](https://azendo.co/skills/neptune-ai/)
* [DVC — skill we assign for](https://azendo.co/skills/dvc/)
* [Feast — skill we assign for](https://azendo.co/skills/feast/)
* [feature stores — skill we assign for](https://azendo.co/skills/feature-stores/)

## Tell us what your roadmap needs Seldon for.

A service delivery manager replies with the disciplines we would assign, the monthly capacity and what the first month looks like.

[All skills we assign for](https://azendo.co/skills/)