@@ -36,10 +36,18 @@ You can find more details about wrapping a model with seldon-core [here](https:/
## Deploying the model to your Kubeflow cluster
We need to have seldon component deployed, you can deploy the model once trained using a pre-defined ksonnet component, similar to [this](https://github.com/kubeflow/examples/blob/master/pytorch_mnist/ks_app/components/serving_model.jsonnet) example.
We need to setup our own environment `${KF_ENV}` (e.g., 'default') and modify the Ksonnet component
Create an environment variable, `${KF_ENV}`, to represent a conceptual
deployment environment such as development, test, staging, or production, as
defined by ksonnet. For this example, we use the `default` environment. You can
read more about Kubeflow's use of ksonnet in the Kubeflow
Seldon allows complex runtime graphs for model inference to be deployed. Some example prototypes have been provided to help you get started. Follow the [Seldon docs](https://github.com/SeldonIO/seldon-core/blob/master/docs/wrappers/readme.md) to wrap your model code into an image that can be managed by Seldon. In the examples below we will use a model image ```seldonio/mock_classifier``` ; replace this with your actual model image. You will also need to choose between the v1alpha2 and v1alpha1 prototype examples depending on which version of Seldon you generated above. The following prototypes are available: