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update headings, text, list numbering (#1631)
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reword label text and list of examples
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kbhawkey committed Feb 10, 2020
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{{% stable-status %}}

## Serve a model using Seldon

Seldon comes installed with Kubeflow. Full documentation for running Seldon inference is provided within the [Seldon documentation site](https://docs.seldon.io/projects/seldon-core/en/latest/).
Seldon comes installed with Kubeflow. The [Seldon documentation site](https://docs.seldon.io/projects/seldon-core/en/latest/) provides full documentation for running Seldon inference.

If you have a saved model in a PersistentVolume (PV), Google Cloud Storage bucket or Amazon S3 Storage you can use one of the [prepackaged model servers provided by Seldon](https://docs.seldon.io/projects/seldon-core/en/latest/servers/overview.html).

Seldon also provides [language specific model wrappers](https://docs.seldon.io/projects/seldon-core/en/latest/wrappers/README.html) to wrap your inference code for it to run in Seldon.

### Kubeflow Specifics
## Kubeflow specifics

You need to ensure the namespace where your models will be served has:

1. A Istio gateway named kubeflow-gateway
2. The namespace is labeled with `serving.kubeflow.org/inferenceservice=enabled`

Examples:
* An Istio gateway named kubeflow-gateway
* A label set as `serving.kubeflow.org/inferenceservice=enabled`

Label the namespace for serving:
The following example applies the label `my-namespace` to the namespace for serving:

```
kubectl label namespace my-namespace serving.kubeflow.org/inferenceservice=enabled
```

Create a gateway call kubeflow-gateway in namespace `my-namespace`:
Create a gateway called `kubeflow-gateway` in namespace `my-namespace`:

```
kind: Gateway
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```

Save the above resource and apply it with `kubectl`.


### Examples
## Examples

* [Kubeflow Seldon E2E Pipeline](https://docs.seldon.io/projects/seldon-core/en/latest/examples/kubeflow_seldon_e2e_pipeline.html)
The [Kubeflow Seldon E2E Pipeline](https://docs.seldon.io/projects/seldon-core/en/latest/examples/kubeflow_seldon_e2e_pipeline.html) shows how to build re-usable components for an ML pipeline.

Seldon provides a [large set of example notebooks](https://docs.seldon.io/projects/seldon-core/en/latest/examples/notebooks.html) showing how to run inference code for a wide range of machine learning toolkits.


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