Training a Machine Learning (ML) model is only one step in the ML lifecycle. There's no purpose to ML if you cannot get a response from your model. You must be able to host your trained model for inference. There's a variety of hosting/deployment options that can be used for ML, with one of the most popular being TensorFlow Serving. TensorFlow Serving helps take your trained model's artifacts and host it for inference.

If you are familiar with Kubeflow, you know KFServing as the platform's model server and inference engine. In September last year, the KFServing project has gone through a transformation to become KServe. KServe is now an independent component graduating from the Kubeflow project, apart from the name change. The separation allows KServe to evolve as a separate, cloud native inference engine deployed as a standalone model server. Of course, it will continue to have tight integration with Kubeflow, but they would be treated and maintained as independent open source projects.