Serve Custom Models¶
This feature is a Tech Preview, so it is not fully supported by Arrikto and may not be functionally complete. While it is not intended for production use, we encourage you to try it out and provide us feedback.
Read our Tech Preview Policy for more information.
Serving custom models is about building custom model servers when off-the-shelf model servers do not fit your needs. You can package the custom model servers you create in docker images and deploy them using KServe.
Kale exposes a
serve API that allows you to create an
- combining a Kubeflow artifact ID for the
predictorcomponent with a docker image for the
transformercomponent, and vice versa.
- using a docker image that packages the model and its dependencies for the
predictorcomponent, and - if needed - a docker image that packages the
transformercomponent and its dependencies.
- passing a full container spec to configure the docker images, for both the
The following guides will walk you through using the Kale
serve API to
instantly serve custom models without having to worry about writing your own
.yaml files or building docker images for everything, given that you can
reuse the Kubeflow artifacts you have already created.